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"id": "E0001",
"date": "1943",
"date_precision": "year",
"axis": "0",
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"actor": "Warren McCulloch, Walter Pitts",
"title": "A logical calculus of the ideas immanent in nervous activity",
"event": "A logical calculus of the ideas immanent in nervous activity — presented a formal neuron model that operates as a binary logic element",
"significance": "The first attempt to formalize the activity of nerve cells as logical operations, and the mathematical starting point of artificial neural networks",
"source_url": "https://link.springer.com/article/10.1007/BF02478259",
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"date": "1949",
"date_precision": "year",
"axis": "0",
"axis_name": "Prehistory — From Neural Networks to the Transformer",
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"actor": "Donald O. Hebb",
"title": "Hebb publishes The Organization of Behavior",
"event": "Published The Organization of Behavior — proposed a learning rule in which connections between neurons that fire together are strengthened",
"significance": "The prototype of the idea of letting data change the weights. The theoretical root of later learning rules for neural networks",
"source_url": "https://en.wikipedia.org/wiki/Organization_of_Behavior",
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{
"id": "E0003",
"date": "1950-10",
"date_precision": "month",
"axis": "0",
"axis_name": "Prehistory — From Neural Networks to the Transformer",
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"actor": "Alan Turing",
"title": "Computing Machinery and Intelligence",
"event": "Computing Machinery and Intelligence — proposed the imitation game (the Turing test)",
"significance": "Set up a framework for judging machine intelligence by behavior rather than by internal structure. The prototype of today's debates over benchmarks",
"source_url": "https://academic.oup.com/mind/article/LIX/236/433/986238",
"source_type": "primary",
"claim_type": "fact",
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{
"id": "E0004",
"date": "1955-08-31",
"date_precision": "day",
"axis": "0",
"axis_name": "Prehistory — From Neural Networks to the Transformer",
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"actor": "John McCarthy, Marvin Minsky, Nathaniel Rochester, Claude Shannon",
"title": "McCarthy, Minsky, Rochester and Shannon write the Dartmouth proposal",
"event": "Wrote the proposal for the Dartmouth Summer Research Project — the first use of the term \"artificial intelligence\"",
"significance": "The document that gave the field its name. It led to the workshop the following year, in 1956",
"source_url": "http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html",
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"claim_type": "fact",
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{
"id": "E0005",
"date": "1958",
"date_precision": "year",
"axis": "0",
"axis_name": "Prehistory — From Neural Networks to the Transformer",
"also_axes": "",
"actor": "Frank Rosenblatt",
"title": "The Perceptron",
"event": "The Perceptron — presented a trainable artificial neuron and the perceptron learning algorithm",
"significance": "The first learning procedure to update weights from errors. Psychological Review 65(6) 386–408",
"source_url": "https://en.wikipedia.org/wiki/Perceptron",
"source_type": "secondary",
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{
"id": "E0006",
"date": "1969",
"date_precision": "year",
"axis": "0",
"axis_name": "Prehistory — From Neural Networks to the Transformer",
"also_axes": "",
"actor": "Marvin Minsky, Seymour Papert",
"title": "Minsky and Papert publish Perceptrons",
"event": "Published Perceptrons — proved that a single-layer perceptron cannot solve linearly inseparable problems such as XOR",
"significance": "Pinned down the limits mathematically, triggering a slump in neural network research that lasted 10 years or more",
"source_url": "https://mitpress.mit.edu/9780262630221/perceptrons/",
"source_type": "primary",
"claim_type": "fact",
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{
"id": "E0007",
"date": "1974",
"date_precision": "year",
"axis": "0",
"axis_name": "Prehistory — From Neural Networks to the Transformer",
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"actor": "Paul Werbos",
"title": "Werbos's Harvard PhD thesis describes backpropagation for multilayer networks",
"event": "Described error backpropagation training for multilayer neural networks in his Harvard PhD thesis",
"significance": "Regarded as the first description, predating the 1986 rediscovery. The reference point in the debate over priority for backpropagation",
"source_url": "https://en.wikipedia.org/wiki/Paul_Werbos",
"source_type": "secondary",
"claim_type": "fact",
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{
"id": "E0008",
"date": "1980-04",
"date_precision": "month",
"axis": "0",
"axis_name": "Prehistory — From Neural Networks to the Transformer",
"also_axes": "",
"actor": "Kunihiko Fukushima",
"title": "Fukushima publishes the Neocognitron",
"event": "Published the Neocognitron — a hierarchical pattern-recognition neural network invariant to shifts in position",
"significance": "Introduced local receptive fields and a hierarchy of simple and complex cells, becoming the direct predecessor of the CNN architecture",
"source_url": "https://link.springer.com/article/10.1007/BF00344251",
"source_type": "primary",
"claim_type": "fact",
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{
"id": "E0009",
"date": "1982-04",
"date_precision": "month",
"axis": "0",
"axis_name": "Prehistory — From Neural Networks to the Transformer",
"also_axes": "",
"actor": "John J. Hopfield",
"title": "Neural networks and physical systems with emergent collective computational abilities",
"event": "Published Neural networks and physical systems with emergent collective computational abilities",
"significance": "Interpreted neural networks as energy-minimizing physical systems and linked them to associative memory and optimization, contributing to the revival of research",
"source_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC346238",
"source_type": "primary",
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{
"id": "E0010",
"date": "1986-10-09",
"date_precision": "day",
"axis": "0",
"axis_name": "Prehistory — From Neural Networks to the Transformer",
"also_axes": "",
"actor": "David Rumelhart, Geoffrey Hinton, Ronald Williams",
"title": "Learning representations by back-propagating errors",
"event": "Learning representations by back-propagating errors — Nature 323(6088) 533–536",
"significance": "Showed that hidden layers learn useful internal representations for a task on their own, making the training of multilayer neural networks practical",
"source_url": "https://www.nature.com/articles/323533a0",
"source_type": "primary",
"claim_type": "fact",
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{
"id": "E0011",
"date": "1989",
"date_precision": "year",
"axis": "0",
"axis_name": "Prehistory — From Neural Networks to the Transformer",
"also_axes": "",
"actor": "George Cybenko",
"title": "Approximation by superpositions of a sigmoidal function",
"event": "Approximation by superpositions of a sigmoidal function — proved the universal approximation theorem",
"significance": "Proved that a single hidden layer can approximate any continuous function, providing a theoretical basis for expressive power",
"source_url": "https://link.springer.com/article/10.1007/BF02551274",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
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{
"id": "E0012",
"date": "1989",
"date_precision": "year",
"axis": "0",
"axis_name": "Prehistory — From Neural Networks to the Transformer",
"also_axes": "",
"actor": "Kurt Hornik, Maxwell Stinchcombe, Halbert White",
"title": "Multilayer feedforward networks are universal approximators",
"event": "Multilayer feedforward networks are universal approximators — extended the universal approximation theorem to general conditions",
"significance": "Together with Cybenko, established the foundation for the approximation capability of multilayer feedforward neural networks",
"source_url": "https://en.wikipedia.org/wiki/Universal_approximation_theorem",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
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{
"id": "E0013",
"date": "1989-12",
"date_precision": "month",
"axis": "0",
"axis_name": "Prehistory — From Neural Networks to the Transformer",
"also_axes": "",
"actor": "Yann LeCun et al. (AT&T Bell Labs)",
"title": "Backpropagation Applied to Handwritten Zip Code Recognition",
"event": "Backpropagation Applied to Handwritten Zip Code Recognition — applied a convolutional neural network trained with backpropagation to zip code recognition",
"significance": "An early case of combining weight sharing with backpropagation to apply a neural network to a real-world task",
"source_url": "https://en.wikipedia.org/wiki/Convolutional_neural_network",
"source_type": "secondary",
"claim_type": "fact",
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{
"id": "E0014",
"date": "1991",
"date_precision": "year",
"axis": "0",
"axis_name": "Prehistory — From Neural Networks to the Transformer",
"also_axes": "",
"actor": "Sepp Hochreiter",
"title": "Hochreiter's TU Munich thesis identifies the vanishing gradient problem",
"event": "Identified the vanishing gradient problem in a thesis at the Technical University of Munich",
"significance": "The first to show that the learning signal vanishes as depth and sequence length grow. The problem definition behind LSTM and residual connections",
"source_url": "https://en.wikipedia.org/wiki/Vanishing_gradient_problem",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
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{
"id": "E0015",
"date": "1994-03",
"date_precision": "month",
"axis": "0",
"axis_name": "Prehistory — From Neural Networks to the Transformer",
"also_axes": "",
"actor": "Yoshua Bengio, Patrice Simard, Paolo Frasconi",
"title": "Learning long-term dependencies with gradient descent is difficult",
"event": "Learning long-term dependencies with gradient descent is difficult — IEEE TNN 5(2) 157–166",
"significance": "Confirmed in theory and experiments that recurrent neural networks struggle to learn long-term dependencies, motivating the development of later architectures",
"source_url": "https://en.wikipedia.org/wiki/Vanishing_gradient_problem",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
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{
"id": "E0016",
"date": "1997",
"date_precision": "year",
"axis": "0",
"axis_name": "Prehistory — From Neural Networks to the Transformer",
"also_axes": "",
"actor": "Sepp Hochreiter, Jürgen Schmidhuber",
"title": "Hochreiter and Schmidhuber publish Long Short-Term Memory",
"event": "Published Long Short-Term Memory — Neural Computation 9(8) 1735–1780",
"significance": "Used gates to mitigate vanishing gradients, making it possible to learn long sequences. The standard sequence model before the Transformer",
"source_url": "https://www.bioinf.jku.at/publications/older/2604.pdf",
"source_type": "primary",
"claim_type": "fact",
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{
"id": "E0017",
"date": "1998",
"date_precision": "year",
"axis": "0",
"axis_name": "Prehistory — From Neural Networks to the Transformer",
"also_axes": "",
"actor": "Yann LeCun, Léon Bottou, Yoshua Bengio, Patrick Haffner",
"title": "Gradient-based learning applied to document recognition",
"event": "Gradient-based learning applied to document recognition — presented LeNet-5 and MNIST",
"significance": "Established a practical CNN architecture combining convolution, pooling and fully connected layers, and made MNIST the standard testbed",
"source_url": "https://en.wikipedia.org/wiki/LeNet",
"source_type": "secondary",
"claim_type": "fact",
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{
"id": "E0018",
"date": "2003",
"date_precision": "year",
"axis": "0",
"axis_name": "Prehistory — From Neural Networks to the Transformer",
"also_axes": "",
"actor": "Yoshua Bengio et al.",
"title": "A Neural Probabilistic Language Model",
"event": "A Neural Probabilistic Language Model — JMLR 3, 1137–1155",
"significance": "Proposed a neural language model that learns words as dense vectors. The direct predecessor of the embedding lineage that includes word2vec",
"source_url": "https://www.jmlr.org/papers/v3/bengio03a.html",
"source_type": "primary",
"claim_type": "fact",
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{
"id": "E0019",
"date": "2006",
"date_precision": "year",
"axis": "0",
"axis_name": "Prehistory — From Neural Networks to the Transformer",
"also_axes": "",
"actor": "Geoffrey Hinton, Simon Osindero, Yee-Whye Teh",
"title": "A Fast Learning Algorithm for Deep Belief Nets",
"event": "A Fast Learning Algorithm for Deep Belief Nets — greedy layer-wise pretraining followed by fine-tuning",
"significance": "Showed once again that deep networks can be trained, sparking the revival of deep learning in the 2000s",
"source_url": "https://www.cs.toronto.edu/~hinton/absps/fastnc.pdf",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
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{
"id": "E0020",
"date": "2006-12",
"date_precision": "month",
"axis": "0",
"axis_name": "Prehistory — From Neural Networks to the Transformer",
"also_axes": "",
"actor": "Yoshua Bengio et al.",
"title": "Greedy Layer-Wise Training of Deep Networks",
"event": "Greedy Layer-Wise Training of Deep Networks — presented at NIPS 2006",
"significance": "Showed experimentally that layer-wise unsupervised pretraining eases the difficulty of training",
"source_url": "https://papers.nips.cc/paper_files/paper/2006/hash/5da713a690c067105aeb2fae32403405-Abstract.html",
"source_type": "primary",
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{
"id": "E0021",
"date": "2007-02-15",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "",
"actor": "NVIDIA",
"title": "NVIDIA releases the first CUDA SDK",
"event": "First public release of the CUDA SDK",
"significance": "Lowered the barrier to general-purpose computing on GPUs — the software foundation that all of deep learning later rode on",
"source_url": "https://en.wikipedia.org/wiki/CUDA",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
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{
"id": "E0022",
"date": "2009-06",
"date_precision": "month",
"axis": "0",
"axis_name": "Prehistory — From Neural Networks to the Transformer",
"also_axes": "",
"actor": "Jia Deng, Li Fei-Fei et al. (Princeton University)",
"title": "ImageNet: A Large-Scale Hierarchical Image Database",
"event": "ImageNet: A Large-Scale Hierarchical Image Database — presented at CVPR 2009",
"significance": "Released a large-scale image dataset based on the WordNet hierarchy, creating the recognition that data is the bottleneck",
"source_url": "https://www.image-net.org/static_files/papers/imagenet_cvpr09.pdf",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0023",
"date": "2009-06",
"date_precision": "month",
"axis": "0",
"axis_name": "Prehistory — From Neural Networks to the Transformer",
"also_axes": "",
"actor": "Rajat Raina, Anand Madhavan, Andrew Y. Ng (Stanford University)",
"title": "Large-scale Deep Unsupervised Learning using Graphics Processors",
"event": "Large-scale Deep Unsupervised Learning using Graphics Processors — presented at ICML 2009",
"significance": "An early case of applying GPUs to deep learning training in earnest. The starting point of the premise that compute is a resource",
"source_url": "http://robotics.stanford.edu/~ang/papers/icml09-LargeScaleUnsupervisedDeepLearningGPU.pdf",
"source_type": "primary",
"claim_type": "fact",
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{
"id": "E0024",
"date": "2010-03-01",
"date_precision": "day",
"axis": "0",
"axis_name": "Prehistory — From Neural Networks to the Transformer",
"also_axes": "",
"actor": "Dan Ciresan et al. (IDSIA)",
"title": "Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition",
"event": "Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition released on arXiv",
"significance": "Showed that GPU acceleration alone, without unsupervised pretraining, delivered top performance, reviving the purely supervised learning approach",
"source_url": "https://arxiv.org/abs/1003.0358",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0025",
"date": "2010-05",
"date_precision": "month",
"axis": "0",
"axis_name": "Prehistory — From Neural Networks to the Transformer",
"also_axes": "",
"actor": "Xavier Glorot, Yoshua Bengio",
"title": "Understanding the difficulty of training deep feedforward neural networks",
"event": "Understanding the difficulty of training deep feedforward neural networks — presented at AISTATS 2010",
"significance": "Showed that activation functions and initialization determine training stability. The origin of Glorot initialization",
"source_url": "http://proceedings.mlr.press/v9/glorot10a.html",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0026",
"date": "2010-05",
"date_precision": "month",
"axis": "0",
"axis_name": "Prehistory — From Neural Networks to the Transformer",
"also_axes": "",
"actor": "ImageNet",
"title": "ImageNet holds the first ILSVRC",
"event": "Held the first ILSVRC — training data released in May; results announced at an ECCV workshop in September",
"significance": "A standard annual competition in large-scale image classification began. It later became the stage for deep learning's breakthrough",
"source_url": "https://www.image-net.org/challenges/LSVRC/2010/index.php",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
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{
"id": "E0027",
"date": "2010-06",
"date_precision": "month",
"axis": "0",
"axis_name": "Prehistory — From Neural Networks to the Transformer",
"also_axes": "",
"actor": "Vinod Nair, Geoffrey Hinton",
"title": "Rectified Linear Units Improve Restricted Boltzmann Machines",
"event": "Rectified Linear Units Improve Restricted Boltzmann Machines — presented at ICML 2010",
"significance": "An early paper demonstrating the advantages of ReLU, which later became the standard activation function",
"source_url": "https://www.cs.toronto.edu/~hinton/absps/reluICML.pdf",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
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{
"id": "E0028",
"date": "2010-06",
"date_precision": "month",
"axis": "0",
"axis_name": "Prehistory — From Neural Networks to the Transformer",
"also_axes": "",
"actor": "James Bergstra et al. (Université de Montréal)",
"title": "Bergstra et al. present Theano",
"event": "Presented Theano at SciPy 2010 — a Python compiler for mathematical expressions that supports symbolic differentiation and GPU computation",
"significance": "The beginning of a setup in which frameworks determine the speed of research. Influenced the design of later deep learning libraries",
"source_url": "https://github.com/Theano/Theano/blob/master/doc/citation.txt",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
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{
"id": "E0029",
"date": "2011-01",
"date_precision": "month",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "",
"actor": "Hervé Jégou et al. (INRIA)",
"title": "Jégou et al. publish Product Quantization for Nearest Neighbor Search",
"event": "Published Product Quantization for Nearest Neighbor Search — decomposed the space into a Cartesian product of low-dimensional subspaces to compress vectors into short codes, evaluated at a scale of 2 billion vectors",
"significance": "The origin point of the IVF-PQ family. The compressed indexes of FAISS, ScaNN and most vector DBs descend directly from this paper",
"source_url": "https://inria.hal.science/inria-00514462",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
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{
"id": "E0030",
"date": "2011-08",
"date_precision": "month",
"axis": "0",
"axis_name": "Prehistory — From Neural Networks to the Transformer",
"also_axes": "",
"actor": "Dan Ciresan et al. (IDSIA)",
"title": "Multi-column deep neural network beats humans with 99.46% at IJCNN 2011 GTSRB",
"event": "At the IJCNN 2011 GTSRB traffic sign recognition competition, a multi-column deep neural network scored 99.46%, above the human average of 98.84%",
"significance": "An officially recognized benchmark record of a neural network surpassing humans on a specific visual recognition task",
"source_url": "https://benchmark.ini.rub.de/gtsrb_results.html",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
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{
"id": "E0031",
"date": "2011-12-29",
"date_precision": "day",
"axis": "0",
"axis_name": "Prehistory — From Neural Networks to the Transformer",
"also_axes": "",
"actor": "Quoc V. Le, Jeff Dean, Andrew Y. Ng et al. (Google Brain)",
"title": "Building high-level features using large scale unsupervised learning",
"event": "Building high-level features using large scale unsupervised learning released on arXiv — unsupervised learning on 10 million YouTube frames",
"significance": "Showed that high-level concepts emerge from nothing more than large-scale unlabeled data and large-scale compute. A symbol of Google's investment in deep learning",
"source_url": "https://arxiv.org/abs/1112.6209",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
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{
"id": "E0032",
"date": "2012",
"date_precision": "year",
"axis": "0",
"axis_name": "Prehistory — From Neural Networks to the Transformer",
"also_axes": "",
"actor": "Geoffrey Hinton, Li Deng, Dong Yu et al. (University of Toronto, Microsoft, Google, IBM)",
"title": "Deep Neural Networks for Acoustic Modeling in Speech Recognition",
"event": "Deep Neural Networks for Acoustic Modeling in Speech Recognition — a joint review by four research groups",
"significance": "Separate industry groups confirmed the same shift (HMM→DNN), and deep learning became the industry standard for speech recognition",
"source_url": "https://research.google/pubs/deep-neural-networks-for-acoustic-modeling-in-speech-recognition/",
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"claim_type": "fact",
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{
"id": "E0033",
"date": "2012-06-24",
"date_precision": "day",
"axis": "0",
"axis_name": "Prehistory — From Neural Networks to the Transformer",
"also_axes": "",
"actor": "Yoshua Bengio, Aaron Courville, Pascal Vincent",
"title": "Representation Learning: A Review and New Perspectives",
"event": "Representation Learning: A Review and New Perspectives released on arXiv",
"significance": "Framed representation learning as a single problem, becoming the field's standard reference from then on",
"source_url": "https://arxiv.org/abs/1206.5538",
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"claim_type": "fact",
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{
"id": "E0034",
"date": "2012-07-03",
"date_precision": "day",
"axis": "0",
"axis_name": "Prehistory — From Neural Networks to the Transformer",
"also_axes": "",
"actor": "Geoffrey Hinton et al. (University of Toronto)",
"title": "Improving neural networks by preventing co-adaptation of feature detectors",
"event": "Improving neural networks by preventing co-adaptation of feature detectors released on arXiv — proposed dropout",
"significance": "Curbed overfitting with simple regularization and later became a standard technique",
"source_url": "https://arxiv.org/abs/1207.0580",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
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{
"id": "E0035",
"date": "2012-10",
"date_precision": "month",
"axis": "0",
"axis_name": "Prehistory — From Neural Networks to the Transformer",
"also_axes": "",
"actor": "SuperVision (Krizhevsky, Sutskever, Hinton, University of Toronto)",
"title": "SuperVision wins ILSVRC 2012 with a 15.3% top-5 error rate",
"event": "Won ILSVRC 2012 with a top-5 error rate of 15.3%; the results were announced at the ECCV 2012 workshop",
"significance": "The turning point at which, with a wide margin over second place, deep CNNs became the mainstream methodology of computer vision",
"source_url": "https://www.image-net.org/challenges/LSVRC/2012/results.html",
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{
"id": "E0036",
"date": "2012-12",
"date_precision": "month",
"axis": "0",
"axis_name": "Prehistory — From Neural Networks to the Transformer",
"also_axes": "",
"actor": "Alex Krizhevsky, Ilya Sutskever, Geoffrey Hinton",
"title": "ImageNet Classification with Deep Convolutional Neural Networks (AlexNet)",
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"title": "Human-level control through deep reinforcement learning",
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"title": "Google publishes Distilling the Knowledge in a Neural Network on arXiv",
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"actor": "Olaf Ronneberger, Philipp Fischer, Thomas Brox (University of Freiburg)",
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"title": "Google open-sources TensorFlow under Apache 2.0",
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"actor": "Kaiming He et al. (Microsoft Research Asia)",
"title": "He et al. publish Deep Residual Learning for Image Recognition (ResNet) on arXiv",
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"significance": "Made training of very deep networks possible through residual connections. A basic component of most later architectures, including the Transformer",
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{
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"actor": "OpenAI",
"title": "Nonprofit research lab OpenAI is announced, with $1 billion pledged",
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"actor": "Google DeepMind",
"title": "Mastering the game of Go with deep neural networks and tree search",
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"actor": "H. Brendan McMahan et al. (Google)",
"title": "Google presents FedAvg / federated learning",
"event": "Presented FedAvg / federated learning — training without collecting data distributed across devices, with 10 to 100 times fewer communication rounds than synchronous SGD",
"significance": "Opened up the path to on-device training on mobile",
"source_url": "https://arxiv.org/abs/1602.05629",
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"actor": "Google DeepMind, Lee Sedol",
"title": "AlphaGo–Lee Sedol five-game match in Seoul ends (Mar 9–15)",
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"significance": "An event that changed public perception of what AI can do. It became the backdrop for later discussions of investment and policy",
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{
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"actor": "Yu. A. Malkov, D. A. Yashunin",
"title": "Malkov and Yashunin present HNSW",
"event": "Presented HNSW — approximate nearest neighbor search at logarithmic scale using hierarchical navigable small world graphs",
"significance": "The default indexing algorithm of almost every vector DB today",
"source_url": "https://arxiv.org/abs/1603.09320",
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"actor": "NVIDIA",
"title": "NVIDIA releases NCCL",
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"significance": "The standard layer that optimizes all-reduce for the hardware topology. All data-parallel, ZeRO and FSDP training runs on top of it",
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"actor": "Google",
"title": "Google first unveils the TPU (Tensor Processing Unit)",
"event": "Unveiled the TPU (Tensor Processing Unit) for the first time",
"significance": "The start of the path toward silicon dedicated to neural networks rather than general-purpose GPUs",
"source_url": "https://cloud.google.com/blog/products/ai-machine-learning/google-supercharges-machine-learning-tasks-with-custom-chip",
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"actor": "Martín Abadi et al. (Google)",
"title": "Google presents Deep Learning with Differential Privacy (DP-SGD)",
"event": "Presented Deep Learning with Differential Privacy (DP-SGD)",
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"actor": "Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey Hinton",
"title": "Ba, Kiros and Hinton publish Layer Normalization on arXiv",
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"actor": "Facebook AI Research (Soumith Chintala et al.)",
"title": "Facebook AI Research first releases PyTorch",
"event": "First public release of PyTorch",
"significance": "With imperative execution, began to change the framework landscape in the research community",
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"actor": "Google DeepMind",
"title": "Google DeepMind announces WaveNet",
"event": "Announced WaveNet — a neural network that directly generates raw audio waveforms",
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"source_url": "https://deepmind.google/blog/wavenet-a-generative-model-for-raw-audio/",
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"actor": "Yonghui Wu et al. (Google)",
"title": "Google's Neural Machine Translation System (GNMT) released on arXiv",
"event": "Google's Neural Machine Translation System (GNMT) released on arXiv",
"significance": "A system description of attention-based seq2seq implemented at production scale",
"source_url": "https://arxiv.org/abs/1609.08144",
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"actor": "Google",
"title": "Google says GNMT now handles 100% of live Chinese→English translation",
"event": "Announced that GNMT had been applied to 100% of its live Chinese→English translation service",
"significance": "The point at which neural machine translation crossed over from research to commercial service",
"source_url": "https://research.google/blog/a-neural-network-for-machine-translation-at-production-scale/",
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"date": "2016-10-31",
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"actor": "Nal Kalchbrenner et al. (DeepMind)",
"title": "DeepMind publishes Neural Machine Translation in Linear Time (ByteNet) on arXiv",
"event": "Neural Machine Translation in Linear Time (ByteNet) released on arXiv",
"significance": "An alternative that processed sequences with dilated convolutions instead of recurrence. One branch of the race to move beyond recurrence",
"source_url": "https://arxiv.org/abs/1610.10099",
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"actor": "Barret Zoph, Quoc V. Le (Google Brain)",
"title": "Google Brain publishes Neural Architecture Search with Reinforcement Learning",
"event": "Neural Architecture Search with Reinforcement Learning released on arXiv",
"significance": "The paper that turned architecture design itself into a search problem",
"source_url": "https://arxiv.org/abs/1611.01578",
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{
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"date": "2017-01-23",
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"actor": "Shazeer et al. (Google Brain)",
"title": "Sparsely-Gated Mixture-of-Experts Layer",
"event": "Sparsely-Gated Mixture-of-Experts Layer",
"significance": "The first demonstration, through conditional computation, of an architecture whose total parameters far exceed its active parameters; the ancestor of every MoE",
"source_url": "https://arxiv.org/abs/1701.06538",
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{
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"actor": "Martin Arjovsky, Soumith Chintala, Léon Bottou",
"title": "Arjovsky, Chintala and Bottou publish Wasserstein GAN on arXiv",
"event": "Wasserstein GAN released on arXiv",
"significance": "Redefined the instability of GAN training as a problem of the distance function",
"source_url": "https://arxiv.org/abs/1701.07875",
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{
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"actor": "Jeff Johnson, Matthijs Douze, Hervé Jégou (FAIR)",
"title": "FAIR presents FAISS",
"event": "Presented FAISS — GPU k-selection reaches up to 55% of theoretical peak. Handled 1 billion vectors on 4 Titan X GPUs in under 12 hours",
"significance": "The library that brought large-scale vector search into the realm of practical use",
"source_url": "https://arxiv.org/abs/1702.08734",
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"actor": "Google (TensorFlow)",
"title": "Google releases XLA",
"event": "Released XLA — JIT-compiles computation graphs to fuse operations and generate native code, for up to 50% speedups on GPUs",
"significance": "The starting point of the ML compiler race. The layer that made TPUs practical and the foundation of all of JAX",
"source_url": "https://developers.googleblog.com/2017/03/xla-tensorflow-compiled.html",
"source_type": "primary",
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},
{
"id": "E0074",
"date": "2017-03-09",
"date_precision": "day",
"axis": "0",
"axis_name": "Prehistory — From Neural Networks to the Transformer",
"also_axes": "",
"actor": "Chelsea Finn, Pieter Abbeel, Sergey Levine (UC Berkeley)",
"title": "Finn, Abbeel and Levine publish Model-Agnostic Meta-Learning (MAML) on arXiv",
"event": "Model-Agnostic Meta-Learning (MAML) released on arXiv",
"significance": "Proposed architecture-agnostic meta-learning, opening up research on few-shot adaptation",
"source_url": "https://arxiv.org/abs/1703.03400",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0075",
"date": "2017-03-30",
"date_precision": "day",
"axis": "0",
"axis_name": "Prehistory — From Neural Networks to the Transformer",
"also_axes": "",
"actor": "Jun-Yan Zhu et al. (UC Berkeley)",
"title": "Zhu et al. publish CycleGAN on arXiv",
"event": "CycleGAN released on arXiv — domain translation with unpaired data",
"significance": "A design that trains without paired data, lowering the data requirements of generative models",
"source_url": "https://arxiv.org/abs/1703.10593",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0076",
"date": "2017-04-16",
"date_precision": "day",
"axis": "0",
"axis_name": "Prehistory — From Neural Networks to the Transformer",
"also_axes": "",
"actor": "Norman P. Jouppi et al. (Google)",
"title": "In-Datacenter Performance Analysis of a Tensor Processing Unit",
"event": "In-Datacenter Performance Analysis of a Tensor Processing Unit released on arXiv (ISCA 2017)",
"significance": "Disclosed the design and measured performance of the first-generation TPU, becoming the reference document for discussions of dedicated accelerators",
"source_url": "https://arxiv.org/abs/1704.04760",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0077",
"date": "2017-05-08",
"date_precision": "day",
"axis": "0",
"axis_name": "Prehistory — From Neural Networks to the Transformer",
"also_axes": "",
"actor": "Jonas Gehring et al. (Facebook AI Research)",
"title": "Convolutional Sequence to Sequence Learning (ConvS2S) released on arXiv",
"event": "Convolutional Sequence to Sequence Learning (ConvS2S) released on arXiv",
"significance": "Performed translation with convolutions alone, showing the race to move beyond recurrence just before the Transformer",
"source_url": "https://arxiv.org/abs/1705.03122",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0078",
"date": "2017-06-12",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Google (Vaswani et al.)",
"title": "Google releases the Transformer paper Attention Is All You Need",
"event": "Released the Transformer paper Attention Is All You Need",
"significance": "Self-attention alone, without recurrence, made parallel training possible, establishing the architectural premise for large-scale pretraining",
"source_url": "https://arxiv.org/abs/1706.03762",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0079",
"date": "2017-10-10",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "",
"actor": "Paulius Micikevicius et al. (NVIDIA)",
"title": "NVIDIA presents Mixed Precision Training",
"event": "Presented Mixed Precision Training — FP16 training with an FP32 master copy of the weights and loss scaling, cutting memory by about 50%",
"significance": "The prototype of mixed-precision training. The whole lineage of shrinking precision, FP16→BF16→FP8→FP4, is a variation on these two techniques",
"source_url": "https://arxiv.org/abs/1710.03740",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0080",
"date": "2017-11-11",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Andrej Karpathy",
"title": "Karpathy publishes Software 2.0 on Medium",
"event": "Published Software 2.0 on Medium — argued that the weights of a neural network are themselves the code: programmers specify the behavior and optimization finds the solution",
"significance": "Planted the generational vocabulary of Software 1.0/2.0 and became the prototype for every later Software N.0 discussion",
"source_url": "https://karpathy.medium.com/software-2-0-a64152b37c35",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0081",
"date": "2017-12-05",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "",
"actor": "NVIDIA",
"title": "NVIDIA releases a preview of CUTLASS",
"event": "Released a preview of CUTLASS — a CUDA C++ template library for GEMM",
"significance": "Broke kernels down into composable parts. FlashAttention, Transformer Engine and many modern attention kernels are built on top of it",
"source_url": "https://developer.nvidia.com/blog/cutlass-linear-algebra-cuda/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0082",
"date": "2018",
"date_precision": "year",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "",
"actor": "JAX development team",
"title": "JAX goes open source",
"event": "Open-sourced JAX",
"significance": "With composable grad/jit/vmap transformations and XLA compilation, it formed the standard route for large-scale training on TPUs. The repository states that it is a research project, not an official Google product",
"source_url": "https://github.com/jax-ml/jax",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0083",
"date": "2018-02-12",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "",
"actor": "Tianqi Chen et al. (University of Washington)",
"title": "Chen et al. present TVM",
"event": "Presented TVM — automatic code generation using graph- and operator-level optimization and a learned cost model",
"significance": "The first practical compiler alternative to the monopoly of vendor kernel libraries. Brought the concept of automatic scheduling to the industry",
"source_url": "https://arxiv.org/abs/1802.04799",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0084",
"date": "2018-02-15",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "",
"actor": "Alexander Sergeev et al. (Uber)",
"title": "Uber presents Horovod",
"event": "Presented Horovod — ring all-reduce cut communication overhead between GPUs and reduced the changes needed in user code to a few lines",
"significance": "The turning point that brought distributed training down from research infrastructure to an everyday tool",
"source_url": "https://arxiv.org/abs/1802.05799",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0085",
"date": "2018-03-27",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "",
"actor": "NVIDIA",
"title": "NVIDIA announces NVSwitch and DGX-2",
"event": "Announced NVSwitch and DGX-2 — connects 16 V100 GPUs simultaneously at 2.4 TB/s, with 5 times the bandwidth of a PCIe switch",
"significance": "The layer that made the inside of a node one big GPU. The physical precondition for tensor parallelism becoming practical",
"source_url": "https://nvidianews.nvidia.com/news/nvidia-boosts-worlds-leading-deep-learning-computing-platform-bringing-10x-performance-gain-in-six-months",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0086",
"date": "2018-04-20",
"date_precision": "day",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
"also_axes": "",
"actor": "Alex Wang et al. (NYU and others)",
"title": "Wang et al. release the GLUE benchmark",
"event": "Released the GLUE benchmark",
"significance": "Defined language understanding as a bundle of multiple tasks rather than a single task. The prototype of every later LLM leaderboard",
"source_url": "https://arxiv.org/abs/1804.07461",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1208",
"date": "2018-06-11",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "",
"actor": "OpenAI (Radford et al.)",
"title": "OpenAI publishes the original GPT (GPT-1) paper",
"event": "The GPT (commonly called GPT-1) paper Improving Language Understanding by Generative Pre-Training — a 12-layer decoder-only Transformer pretrained on BooksCorpus, then fine-tuned for each task",
"significance": "Reported that combining unsupervised generative pre-training with supervised fine-tuning improved the state of the art on 9 of the 12 tasks studied",
"source_url": "https://openai.com/index/language-unsupervised/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E1209",
"date": "2018-10-11",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "",
"actor": "Google (Devlin et al.)",
"title": "Google posts the BERT paper on arXiv",
"event": "BERT paper posted on arXiv — pre-training a deep bidirectional Transformer encoder that conditions on both left and right context",
"significance": "Fine-tuned with just one additional output layer, it set new state-of-the-art results on 11 NLP tasks, including GLUE 80.4% and SQuAD v1.1 F1 93.2 (as reported by the authors)",
"source_url": "https://arxiv.org/abs/1810.04805",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E0087",
"date": "2019-01-13",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "",
"actor": "Rodrigo Nogueira, Kyunghyun Cho (NYU)",
"title": "Nogueira and Cho present Passage Re-ranking with BERT",
"event": "Presented Passage Re-ranking with BERT — cross-encoder re-ranking that takes a query-document pair as a single input",
"significance": "The prototype of the reranker lineage. Standardized the two-stage structure that recovers, at a later stage, the precision dense embeddings miss",
"source_url": "https://arxiv.org/abs/1901.04085",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0088",
"date": "2019-02-14",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "OpenAI",
"title": "OpenAI announces GPT-2 (1.5B), withholds full model over misuse concerns",
"event": "Announced GPT-2 (1.5B) and decided not to release the full model because of concerns about misuse",
"significance": "Suggested that natural-language instructions can serve as task specifications, and opened the discourse on the risks of releasing models",
"source_url": "https://openai.com/index/better-language-models/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0089",
"date": "2019-03-13",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Richard Sutton",
"title": "Sutton publishes The Bitter Lesson",
"event": "Published The Bitter Lesson — argued that the biggest lesson of 70 years of AI research is that general methods that leverage computation are ultimately the most effective, by a large margin",
"significance": "Scripture for the scaling camp. Repeatedly cited as the basis for justifying compute over the injection of human knowledge",
"source_url": "https://nparikh.org/assets/pdf/sipa6545/week1-cs-algorithms/sutton-bitter-lesson.pdf",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0090",
"date": "2019-04-17",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "European Parliament, Council of the EU",
"title": "EU adopts the DSM Copyright Directive (EU) 2019/790",
"event": "Adopted the DSM Copyright Directive (EU) 2019/790 — the Article 4 TDM exception and a machine-readable reservation of rights (opt-out). Published in the Official Journal on May 17, 2019; entered into force on June 6, 2019",
"significance": "The EU's legal basis for governing AI training data",
"source_url": "https://eur-lex.europa.eu/eli/dir/2019/790/oj/eng",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0091",
"date": "2019-05-02",
"date_precision": "day",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
"also_axes": "",
"actor": "Alex Wang et al.",
"title": "Wang et al. release SuperGLUE",
"event": "Released SuperGLUE — a harder replacement, introduced because performance on GLUE had surpassed the non-expert human level, leaving less room for research",
"significance": "The first instance of the cycle from benchmark saturation to replacement",
"source_url": "https://arxiv.org/abs/1905.00537",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0092",
"date": "2019-05-29",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "",
"actor": "Dhiraj Kalamkar et al. (Intel)",
"title": "Intel presents A Study of BFLOAT16 for Deep Learning Training",
"event": "Presented A Study of BFLOAT16 for Deep Learning Training — the first comprehensive empirical demonstration that BF16 training is effective",
"significance": "A format that removed FP16's scaling burden. Later the de facto default training precision for the TPU, Ampere and Hopper generations",
"source_url": "https://arxiv.org/abs/1905.12322",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0093",
"date": "2019-08-27",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "",
"actor": "Nils Reimers, Iryna Gurevych (UKP Lab)",
"title": "Reimers and Gurevych present Sentence-BERT",
"event": "Presented Sentence-BERT — cut the time to find the most similar pair among 10,000 sentences from about 65 hours to about 5 seconds",
"significance": "Turned sentence embeddings into a searchable asset",
"source_url": "https://arxiv.org/abs/1908.10084",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0094",
"date": "2019-09-17",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "",
"actor": "Mohammad Shoeybi et al. (NVIDIA)",
"title": "NVIDIA presents Megatron-LM",
"event": "Presented Megatron-LM — implemented intra-layer tensor parallelism in PyTorch alone. Sustained 15.1 PFLOPS with an 8.3B model on 512 GPUs",
"significance": "The standard implementation of large-scale model parallelism",
"source_url": "https://arxiv.org/abs/1909.08053",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0095",
"date": "2019-10-04",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "",
"actor": "Samyam Rajbhandari et al. (Microsoft)",
"title": "Microsoft presents ZeRO",
"event": "Presented ZeRO — partitioning optimizer, gradient and parameter states opened a path to training at 100B+, with models up to 13B possible without model parallelism",
"significance": "Removed the memory barrier to 100B-class training",
"source_url": "https://arxiv.org/abs/1910.02054",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0096",
"date": "2019-10-23",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "",
"actor": "Google",
"title": "Google releases the T5 paper and C4",
"event": "Released the T5 paper and C4",
"significance": "The first large-scale standard corpus built by cleaning Common Crawl with heuristics",
"source_url": "https://arxiv.org/abs/1910.10683",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0097",
"date": "2019-11-05",
"date_precision": "day",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
"also_axes": "",
"actor": "François Chollet",
"title": "Chollet publishes On the Measure of Intelligence, proposing the ARC benchmark",
"event": "Published On the Measure of Intelligence and proposed the ARC benchmark — defined intelligence as skill-acquisition efficiency",
"significance": "Provided the yardstick for the later AGI debate",
"source_url": "https://arxiv.org/abs/1911.01547",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0098",
"date": "2019-11-06",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "",
"actor": "Noam Shazeer",
"title": "Fast Transformer Decoding",
"event": "Fast Transformer Decoding — Multi-Query Attention",
"significance": "Reduced the K/V heads to one, redefining the KV cache as the decoding bottleneck",
"source_url": "https://arxiv.org/abs/1911.02150",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0099",
"date": "2019-12",
"date_precision": "month",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "",
"actor": "Suhas Jayaram Subramanya et al. (Microsoft Research India, CMU)",
"title": "Microsoft Research India and CMU present DiskANN",
"event": "Presented DiskANN — an index of 1 billion points on a single workstation with 64 GB of RAM using the Vamana graph, with 5,000+ QPS and under 3 ms latency on SIFT1B",
"significance": "An SSD-based ANN that broke HNSW's constraint of having to hold everything in memory",
"source_url": "https://neurips.cc/virtual/2019/poster/14308",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0100",
"date": "2019-12-03",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "",
"actor": "Adam Paszke, Sam Gross, Soumith Chintala et al.",
"title": "Paszke et al. publish the PyTorch paper",
"event": "Published the PyTorch paper — demonstrated that imperative, Pythonic execution and performance can coexist",
"significance": "Overturned research practice centered on static-graph frameworks",
"source_url": "https://arxiv.org/abs/1912.01703",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0101",
"date": "2020-01-23",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "OpenAI (Kaplan et al.)",
"title": "Scaling Laws for Neural Language Models",
"event": "Scaling Laws for Neural Language Models",
"significance": "Showed that loss can be predicted as a power law of model size, data and compute, becoming the rationale for large-scale investment",
"source_url": "https://arxiv.org/abs/2001.08361",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0102",
"date": "2020-02-13",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "",
"actor": "Microsoft Research",
"title": "Microsoft Research open-sources DeepSpeed",
"event": "Open-sourced DeepSpeed — claimed training of 100 billion+ parameter models, up to 5 times the throughput, and memory savings of up to 64x on 64 GPUs",
"significance": "The layer that made extremely large-scale training possible without rewriting code for model parallelism",
"source_url": "https://www.microsoft.com/en-us/research/blog/zero-deepspeed-new-system-optimizations-enable-training-models-with-over-100-billion-parameters/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0103",
"date": "2020-02-25",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "",
"actor": "Chris Lattner et al. (Google)",
"title": "Google presents MLIR",
"event": "Presented MLIR — reusable, extensible compiler infrastructure spanning multiple levels of abstraction",
"significance": "The common foundation shared by XLA, TVM, Triton and Mojo. Ended the practice of writing a new compiler for every accelerator",
"source_url": "https://arxiv.org/abs/2002.11054",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0104",
"date": "2020-03-26",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "",
"actor": "Yantao Shen et al. (Amazon AWS, UCLA)",
"title": "Shen et al. present Towards Backward-Compatible Representation Learning",
"event": "Presented Towards Backward-Compatible Representation Learning — proposed BCT, which trains a new embedding model to be compatible with the old index, eliminating re-indexing",
"significance": "The first study to tackle the cost of embedding migration head-on. Framed as a problem the structural debt of having to re-embed the entire corpus every time the model is replaced",
"source_url": "https://arxiv.org/abs/2003.11942",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0105",
"date": "2020-04-10",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "",
"actor": "Vladimir Karpukhin et al. (FAIR, Princeton, University of Washington)",
"title": "Karpukhin et al. present Dense Passage Retrieval",
"event": "Presented Dense Passage Retrieval — absolute gains of 9–19 percentage points over BM25 in top-20 retrieval accuracy",
"significance": "Flipped the default from sparse retrieval (BM25) to dense retrieval",
"source_url": "https://arxiv.org/abs/2004.04906",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0106",
"date": "2020-04-27",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "Omar Khattab et al. (Stanford)",
"title": "Khattab et al. present ColBERT",
"event": "Presented ColBERT — keeps queries and documents as token-level embeddings, then scores them with MaxSim (late interaction)",
"significance": "The starting point of the late interaction lineage, distinct from single-vector retrieval",
"source_url": "https://arxiv.org/abs/2004.12832",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0107",
"date": "2020-05-22",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "FAIR, UCL, NYU (Lewis et al.)",
"title": "FAIR, UCL and NYU publish the original RAG paper",
"event": "The original RAG paper",
"significance": "The canonical text of retrieval-augmented generation, combining parametric and non-parametric memory",
"source_url": "https://arxiv.org/abs/2005.11401",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0108",
"date": "2020-05-28",
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"actor": "OpenAI (Brown et al.)",
"title": "OpenAI publishes the GPT-3 (175B) paper Language Models are Few-Shot Learners",
"event": "The GPT-3 (175B) paper Language Models are Few-Shot Learners",
"significance": "Introduced in-context learning, which performs a task from examples in the prompt alone, without updating parameters",
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"axis_name": "Prompt Engineering",
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"actor": "OpenAI",
"title": "OpenAI releases the OpenAI API in beta (text in, text out)",
"event": "Released the OpenAI API in beta (text in, text out)",
"significance": "Made the approach of programming by showing examples into an official product interface",
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{
"id": "E0110",
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"axis_name": "Model Architecture & Training Methods",
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"actor": "Google (Lepikhin et al.)",
"title": "GShard",
"event": "GShard",
"significance": "Scaled MoE up to a 600B-scale system and established expert parallelism and automatic sharding",
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{
"id": "E0111",
"date": "2020-07-28",
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"axis_name": "Embeddings & Vector Search",
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"actor": "Google Research",
"title": "Google Research open-sources ScaNN",
"event": "Open-sourced ScaNN — applies anisotropic vector quantization; on ann-benchmarks, about 2x the QPS of 11 other libraries at the same accuracy",
"significance": "Redesigned the quantization loss function around inner-product estimation error, pushing the quality-speed frontier of MIPS forward by a generation",
"source_url": "https://research.google/blog/announcing-scann-efficient-vector-similarity-search/",
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{
"id": "E0112",
"date": "2020-09-07",
"date_precision": "day",
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"axis_name": "Evaluation & Benchmarks",
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"actor": "Dan Hendrycks et al.",
"title": "Hendrycks et al. release MMLU (57 tasks)",
"event": "Released MMLU (57 tasks)",
"significance": "Became the de facto standard for measuring the breadth of knowledge of pretrained models, and an essential metric in model announcements for the next 5 years",
"source_url": "https://arxiv.org/abs/2009.03300",
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{
"id": "E0113",
"date": "2020-12-31",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
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"actor": "EleutherAI",
"title": "The Pile",
"event": "The Pile — 825 GiB, 22 subsets",
"significance": "The diversity-curation paradigm. At the same time, it passed copyright burdens such as Books3 on to later models",
"source_url": "https://arxiv.org/abs/2101.00027",
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{
"id": "E0114",
"date": "2021-01-05",
"date_precision": "day",
"axis": "7",
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"actor": "OpenAI",
"title": "OpenAI releases CLIP",
"event": "Released CLIP — a shared embedding space for images and text",
"significance": "Later the text-conditioning backbone of image generation and retrieval models. The contrastive training on 400 million pairs is based on the paper arXiv:2103.00020",
"source_url": "https://openai.com/index/clip/",
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{
"id": "E0115",
"date": "2021-01-05",
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"axis": "7",
"axis_name": "Multimodal",
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"actor": "OpenAI",
"title": "OpenAI releases DALL·E 1",
"event": "Released DALL·E 1 — a GPT-3 variant with 12 billion parameters",
"significance": "The starting point of text-to-image generation",
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{
"id": "E0116",
"date": "2021-01-11",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
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"actor": "Google (Fedus et al.)",
"title": "Switch Transformer",
"event": "Switch Transformer — top-1 routing, 1.6T",
"significance": "Simplified MoE and became the standard reference for routing instability and load-balancing loss",
"source_url": "https://arxiv.org/abs/2101.03961",
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{
"id": "E0117",
"date": "2021-01-18",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
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"actor": "Jie Ren et al. (Microsoft, UC Merced)",
"title": "Microsoft and UC Merced present ZeRO-Offload",
"event": "Presented ZeRO-Offload — offloads optimizer states to the CPU, reaching 40 TFlops for a 10B model on a single V100",
"significance": "The start of the lineage that lowered the hardware barrier to entry for training large models",
"source_url": "https://arxiv.org/abs/2101.06840",
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{
"id": "E0118",
"date": "2021-01-27",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
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"actor": "Pinecone",
"title": "Pinecone launches managed vector database beta, announces $10 million seed",
"event": "Launched a managed vector database in public beta and announced a $10 million seed round",
"significance": "The first commercialization of the vector DB as a standalone product category. Triggered the market formation of Weaviate, Qdrant, Chroma and Milvus",
"source_url": "https://www.pinecone.io/blog/announcing-vector-database/",
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{
"id": "E0119",
"date": "2021-03",
"date_precision": "month",
"axis": "20",
"axis_name": "People & Discourse",
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"actor": "Emily M. Bender, Timnit Gebru et al.",
"title": "Bender, Gebru et al. present On the Dangers of Stochastic Parrots at FAccT",
"event": "Presented On the Dangers of Stochastic Parrots at FAccT — characterized a language model as a system that probabilistically stitches together the linguistic forms it has observed, without any reference to meaning",
"significance": "\"Stochastic parrot\" became the standard vocabulary of the LLM-skeptic camp. Set the baseline for the debate over understanding and meaning",
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{
"id": "E0120",
"date": "2021-03-16",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
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"actor": "Sam Altman",
"title": "Altman posts Moore's Law for Everything",
"event": "Posted Moore's Law for Everything — proposed an American Equity Fund, arguing that capital rather than labor should be taxed",
"significance": "The first case of an AI lab CEO personally designing and putting forward a redistribution policy",
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{
"id": "E0121",
"date": "2021-04-16",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
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"actor": "Samyam Rajbhandari et al. (Microsoft)",
"title": "Microsoft presents ZeRO-Infinity",
"event": "Presented ZeRO-Infinity — uses heterogeneous GPU+CPU+NVMe memory, exceeding 25 petaflops on 512 V100 GPUs",
"significance": "Got around the GPU memory wall by extending into the storage tier. Established the framing that the constraint is bandwidth, not memory",
"source_url": "https://arxiv.org/abs/2104.07857",
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{
"id": "E0122",
"date": "2021-04-17",
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"actor": "Nandan Thakur, Nils Reimers et al. (UKP Lab, Cohere)",
"title": "Thakur, Reimers et al. present BEIR",
"event": "Presented BEIR — a zero-shot heterogeneous benchmark of 18 datasets showing that BM25 is a robust baseline and that dense and sparse models often fall behind out of distribution",
"significance": "A decisive rebuttal of the narrative that dense retrieval had beaten lexical retrieval. The practical grounds for hybrid search becoming the default",
"source_url": "https://arxiv.org/abs/2104.08663",
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{
"id": "E0123",
"date": "2021-04-20",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "",
"actor": "Su et al.",
"title": "RoPE / RoFormer",
"event": "RoPE / RoFormer",
"significance": "Rotary position embedding became the foundation of every later context-extension technique",
"source_url": "https://arxiv.org/abs/2104.09864",
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{
"id": "E0124",
"date": "2021-04-20",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
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"actor": "pgvector project",
"title": "pgvector makes its first release, 0.1.0",
"event": "First release of pgvector, 0.1.0",
"significance": "The start of the path that brought vector search into existing Postgres rather than a separate DB",
"source_url": "https://raw.githubusercontent.com/pgvector/pgvector/master/CHANGELOG.md",
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{
"id": "E0125",
"date": "2021-04-21",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "European Commission (EU)",
"title": "European Commission publishes AI Act proposal COM(2021) 206",
"event": "Published the AI Act legislative proposal COM(2021) 206 — the date of the Commission's proposal",
"significance": "The starting point of the EU's first comprehensive AI regulation",
"source_url": "https://artificialintelligenceact.eu/development/",
"source_type": "secondary",
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{
"id": "E1210",
"date": "2021-05-18",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "",
"actor": "Google",
"title": "Google unveils LaMDA",
"event": "LaMDA (Language Model for Dialogue Applications) unveiled — a Transformer-based conversation model trained on dialogue",
"significance": "Unlike most language models it was trained on dialogue, and Google said fine-tuning after training could significantly improve the sensibleness and specificity of its responses",
"source_url": "https://blog.google/technology/ai/lamda/",
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},
{
"id": "E1211",
"date": "2021-05-25",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "13",
"actor": "Naver",
"title": "Naver unveils HyperCLOVA",
"event": "Unveiled HyperCLOVA at NAVER AI NOW — 204 billion parameters by the company's account, with 97% of its training data in Korean",
"significance": "Presented by Naver as Korea's first hyperscale AI and a means to AI sovereignty; a closed model later succeeded by HyperCLOVA X",
"source_url": "https://www.navercorp.com/en/media/pressReleasesDetail?seq=30588",
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},
{
"id": "E0126",
"date": "2021-06-17",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "Microsoft (Edward J. Hu et al.)",
"title": "LoRA",
"event": "LoRA",
"significance": "Freezes the weights and trains only low-rank matrices, cutting trainable parameters to 1/10,000 for GPT-3 175B, with no increase in inference latency",
"source_url": "https://arxiv.org/abs/2106.09685",
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},
{
"id": "E0127",
"date": "2021-06-23",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
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"actor": "LF AI & Data Foundation / Milvus",
"title": "LF AI & Data Foundation promotes the Milvus project to Graduate level",
"event": "The Milvus project was promoted to Graduate level — adoption by more than 1,000 organizations reported",
"significance": "An event showing that the vector DB could stand as foundation-governed open-source infrastructure rather than a vendor product",
"source_url": "https://lfaidata.foundation/blog/2021/06/23/lf-ai-data-foundation-announces-graduation-of-milvus-project/",
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},
{
"id": "E0128",
"date": "2021-07-12",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
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"actor": "Thibault Formal et al. (Naver Labs Europe)",
"title": "Naver Labs Europe presents SPLADE",
"event": "Presented SPLADE — a first-stage ranker based on explicit sparsity regularization",
"significance": "The strand that made lexical retrieval learnable by neural networks. Broke down the dense-versus-sparse dichotomy",
"source_url": "https://arxiv.org/abs/2107.05720",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
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{
"id": "E0129",
"date": "2021-07-28",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "",
"actor": "OpenAI",
"title": "OpenAI releases Triton",
"event": "Released Triton — researchers with no CUDA experience can write cuBLAS-level FP16 matmul kernels in about 25 lines",
"significance": "Opened kernel writing up from a handful of experts to ordinary researchers",
"source_url": "https://openai.com/index/triton/",
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},
{
"id": "E0130",
"date": "2021-08-27",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "",
"actor": "Press et al.",
"title": "ALiBi",
"event": "ALiBi",
"significance": "Extrapolates with only a linear bias and no position embeddings; one of the two main approaches, alongside RoPE",
"source_url": "https://arxiv.org/abs/2108.12409",
"source_type": "primary",
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{
"id": "E0131",
"date": "2021-09-03",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Google (Wei et al.)",
"title": "FLAN",
"event": "FLAN — instruction tuning",
"significance": "The 137B model outperformed the 175B GPT-3 zero-shot on 20 of 25 tasks",
"source_url": "https://arxiv.org/abs/2109.01652",
"source_type": "primary",
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},
{
"id": "E0132",
"date": "2021-11-21",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "",
"actor": "PyTorch (Meta)",
"title": "PyTorch opens the FSDP2 RFC",
"event": "Opened the FSDP2 RFC — pointed out the limits of the flat parameter structure and proposed per-parameter sharding, a communication-free sharded state dict and mixed FP8 all-gather",
"significance": "A redesign of FSDP1. The precondition that made FP8 training, partial freezing and compiler optimization possible",
"source_url": "https://github.com/pytorch/pytorch/issues/114299",
"source_type": "primary",
"claim_type": "community",
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},
{
"id": "E0133",
"date": "2021-12-02",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "Keshav Santhanam et al. (Stanford)",
"title": "Santhanam et al. present ColBERTv2",
"event": "Presented ColBERTv2 — residual compression and denoised supervision shrink the size of late interaction indexes",
"significance": "The follow-up that brought late interaction down to practically sized indexes",
"source_url": "https://arxiv.org/abs/2112.01488",
"source_type": "primary",
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"added_week": "2026-W35"
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{
"id": "E0134",
"date": "2021-12-20",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "",
"actor": "LMU Munich, Heidelberg University IWR, Runway",
"title": "The Latent Diffusion Model paper",
"event": "The Latent Diffusion Model paper",
"significance": "Diffusion in latent space rather than pixel space sharply cut compute; the direct basis of Stable Diffusion",
"source_url": "https://arxiv.org/abs/2112.10752",
"source_type": "primary",
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{
"id": "E0135",
"date": "2021-12-31",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "Cyberspace Administration of China and 3 other ministries",
"title": "China promulgates provisions on algorithmic recommendation (Order No. 9)",
"event": "Promulgated the Provisions on the Management of Algorithmic Recommendations in Internet Information Services (Order No. 9) — in force from March 1, 2022",
"significance": "An algorithm registry system and the right to refuse personalized recommendations",
"source_url": "https://www.cac.gov.cn/2022-01/04/c_1642894606364259.htm",
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"added_week": "2026-W35"
},
{
"id": "E0136",
"date": "2022-01-27",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "OpenAI",
"title": "OpenAI announces InstructGPT and makes it the API's default model",
"event": "Announced InstructGPT and switched the API's default model to it",
"significance": "OpenAI itself wrote that it unlocked capabilities that had been hard to draw out through prompt engineering alone",
"source_url": "https://openai.com/index/instruction-following/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0137",
"date": "2022-01-28",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Jason Wei et al. (Google Brain)",
"title": "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models",
"event": "Presented Chain-of-Thought Prompting Elicits Reasoning in Large Language Models",
"significance": "The starting point of the entire lineage of changing reasoning performance through prompts alone. With 8 CoT exemplars, the 540B model reached SOTA on GSM8K",
"source_url": "https://arxiv.org/abs/2201.11903",
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},
{
"id": "E0138",
"date": "2022-01-28",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "",
"actor": "Lianmin Zheng et al. (UC Berkeley)",
"title": "Zheng et al. present Alpa",
"event": "Presented Alpa — automated inter-operator and intra-operator parallelism",
"significance": "Automated the search for parallelization strategies. Redefined manual 3D parallelism tuning as a compilation problem",
"source_url": "https://arxiv.org/abs/2201.12023",
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},
{
"id": "E0139",
"date": "2022-02-07",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
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"actor": "Elastic",
"title": "Elasticsearch 8.0 adds HNSW-based approximate kNN search as a technical preview",
"event": "Introduced HNSW-based approximate kNN search in Elasticsearch 8.0 as a technical preview",
"significance": "The point at which existing search engines began absorbing vector search. The starting point of the consolidation debate over whether a dedicated vector DB is needed",
"source_url": "https://www.elastic.co/blog/introducing-approximate-nearest-neighbor-search-in-elasticsearch-8-0",
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},
{
"id": "E0140",
"date": "2022-03-04",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "OpenAI (Ouyang et al.)",
"title": "OpenAI publishes the InstructGPT paper on arXiv",
"event": "Released the InstructGPT paper on arXiv",
"significance": "Showed that outputs of the 1.3B model were preferred to those of the 175B GPT-3, establishing RLHF as the standard procedure for alignment",
"source_url": "https://arxiv.org/abs/2203.02155",
"source_type": "primary",
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},
{
"id": "E0141",
"date": "2022-03-07",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "",
"actor": "Greg Yang et al.",
"title": "muP / Tensor Programs V",
"event": "muP / Tensor Programs V",
"significance": "Transferred hyperparameters tuned on small models zero-shot to large ones, making the practice of getting it right in a single run possible",
"source_url": "https://arxiv.org/abs/2203.03466",
"source_type": "primary",
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},
{
"id": "E0142",
"date": "2022-03-10",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "",
"actor": "Wortsman et al.",
"title": "Model Soups",
"event": "Model Soups",
"significance": "Demonstrated that simply averaging the weights of several fine-tuned models raises performance; the starting point of model merging",
"source_url": "https://arxiv.org/abs/2203.05482",
"source_type": "primary",
"claim_type": "fact",
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},
{
"id": "E0143",
"date": "2022-03-10",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Gary Marcus",
"title": "Marcus writes Deep Learning Is Hitting a Wall for Nautilus",
"event": "Wrote Deep Learning Is Hitting a Wall for Nautilus — argued that scaling laws are not universal laws like gravity but merely observations that may not hold forever",
"significance": "\"Hitting a wall\" became the stock phrase of scaling skepticism",
"source_url": "https://nautil.us/deep-learning-is-hitting-a-wall-238440/",
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"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0144",
"date": "2022-03-21",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Xuezhi Wang et al. (Google Research)",
"title": "Google Research presents Self-Consistency",
"event": "Presented Self-Consistency — samples multiple CoT paths and picks the answer they most often agree on",
"significance": "The start of the test-time sampling-and-voting lineage. GSM8K +10%, MultiArith +24%",
"source_url": "https://arxiv.org/abs/2203.11171",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0145",
"date": "2022-03-22",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "",
"actor": "NVIDIA",
"title": "NVIDIA unveils the Hopper H100",
"event": "Unveiled the Hopper H100 — the Transformer Engine dynamically selects FP8 or 16-bit per layer and rescales. NVLink 4 delivers 900 GB/s",
"significance": "The turning point that made FP8 training a hardware-supported feature. The first case of software and silicon bound together as one",
"source_url": "https://developer.nvidia.com/blog/nvidia-hopper-architecture-in-depth/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0146",
"date": "2022-03-23",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "",
"actor": "Paul Barham, Jeff Dean et al. (Google)",
"title": "Google presents Pathways",
"event": "Presented Pathways — a single-controller, asynchronous distributed execution model",
"significance": "The orchestration layer that made PaLM-class training possible, and the execution foundation of the JAX/TPU stack",
"source_url": "https://arxiv.org/abs/2203.12533",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0147",
"date": "2022-03-29",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "",
"actor": "DeepMind (Hoffmann et al.)",
"title": "Chinchilla",
"event": "Chinchilla — derived from over 400 models that parameters and tokens should be scaled in equal proportion",
"significance": "Exposed that the large models of the time were undertrained; the 70B model outperformed the 280B Gopher",
"source_url": "https://arxiv.org/abs/2203.15556",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1212",
"date": "2022-04-04",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "",
"actor": "Google Research",
"title": "Google Research announces PaLM (540B)",
"event": "PaLM (Pathways Language Model) announced — a 540B dense decoder-only Transformer trained on 6,144 TPU v4 chips with Pathways",
"significance": "The first large-scale use of the Pathways system; reported surpassing the few-shot performance of prior large models such as GPT-3, Gopher and Chinchilla on 28 of 29 English NLP tasks",
"source_url": "https://research.google/blog/pathways-language-model-palm-scaling-to-540-billion-parameters-for-breakthrough-performance/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E0148",
"date": "2022-04-29",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "",
"actor": "DeepMind",
"title": "Flamingo",
"event": "Flamingo",
"significance": "The prototype of the few-shot VLM, connecting frozen vision and language models through cross-attention",
"source_url": "https://arxiv.org/abs/2204.14198",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0149",
"date": "2022-05-19",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Antonia Creswell et al. (DeepMind)",
"title": "DeepMind presents Selection-Inference",
"event": "Presented Selection-Inference — splits reasoning into two steps, selection/inference, and repeats them",
"significance": "A lineage of modular, decomposed reasoning rather than single CoT generation",
"source_url": "https://arxiv.org/abs/2205.09712",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0150",
"date": "2022-05-21",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Denny Zhou et al. (Google)",
"title": "Google presents Least-to-Most Prompting",
"event": "Presented Least-to-Most Prompting — breaks a problem into subproblems and solves them in sequence",
"significance": "The prototype of the decomposition-and-planning lineage (Plan-and-Solve, Self-Discover)",
"source_url": "https://arxiv.org/abs/2205.10625",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0151",
"date": "2022-05-24",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Takeshi Kojima et al. (University of Tokyo, Google Research)",
"title": "Kojima et al. present Large Language Models are Zero-Shot Reasoners",
"event": "Presented Large Language Models are Zero-Shot Reasoners — elicited CoT without examples using a single sentence, “Let's think step by step”",
"significance": "CoT without examples. The original of every later zero-shot reasoning prompt",
"source_url": "https://arxiv.org/abs/2205.11916",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0152",
"date": "2022-05-24",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Jaehun Jung et al. (University of Washington, AI2)",
"title": "Jung et al. present Maieutic Prompting",
"event": "Presented Maieutic Prompting — builds a recursive tree of explanations and decides the answer by logical consistency",
"significance": "An early case of treating generated reasoning as something to check and to resolve conflicts in",
"source_url": "https://arxiv.org/abs/2205.11822",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0153",
"date": "2022-05-26",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "",
"actor": "Aditya Kusupati et al. (University of Washington, Google)",
"title": "Kusupati et al. present Matryoshka Representation Learning",
"event": "Presented Matryoshka Representation Learning — a single embedding keeps its performance even when truncated. Embeddings up to 14 times smaller at the same accuracy",
"significance": "A technique that made embedding dimensions adjustable after the fact",
"source_url": "https://arxiv.org/abs/2205.13147",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0154",
"date": "2022-05-27",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "",
"actor": "Xiang Lisa Li et al. (Stanford)",
"title": "Li et al. present Diffusion-LM",
"event": "Presented Diffusion-LM — non-autoregressive text generation that iteratively denoises Gaussian vectors into word vectors",
"significance": "The starting point of the diffusion language model lineage",
"source_url": "https://arxiv.org/abs/2205.14217",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0155",
"date": "2022-05-27",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "",
"actor": "Tri Dao, Daniel Y. Fu et al. (Stanford)",
"title": "Dao et al. present FlashAttention",
"event": "Presented FlashAttention — IO-aware tiling gave a 3x speedup on GPT-2 while keeping attention exact. The first Transformer to beat chance on Path-X (16K)",
"significance": "Effectively opened up the road to long context",
"source_url": "https://arxiv.org/abs/2205.14135",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0156",
"date": "2022-06-09",
"date_precision": "day",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
"also_axes": "",
"actor": "Aarohi Srivastava et al. (450 authors, 132 institutions)",
"title": "Srivastava et al. release BIG-bench (204 tasks)",
"event": "Released BIG-bench (204 tasks)",
"significance": "A community-crowdsourced benchmark. Concluded that performance improves with scale but stays low in absolute terms",
"source_url": "https://arxiv.org/abs/2206.04615",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0157",
"date": "2022-06-15",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "",
"actor": "Wei et al.",
"title": "Emergent Abilities of LLMs",
"event": "Emergent Abilities of LLMs",
"significance": "Its claim that abilities emerge at scale thresholds became a core narrative of the scaling discourse",
"source_url": "https://arxiv.org/abs/2206.07682",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0158",
"date": "2022-06-21",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "",
"actor": "GitHub",
"title": "GitHub makes Copilot generally available (GA)",
"event": "Made Copilot generally available (GA)",
"significance": "The baseline of the pre-agent era — the limits of autocomplete that suggests code but cannot run or check it on its own",
"source_url": "https://github.blog/news-insights/product-news/github-copilot-is-generally-available-to-all-developers/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0159",
"date": "2022-06-27",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Yann LeCun",
"title": "LeCun publishes position paper A Path Towards Autonomous Machine Intelligence",
"event": "Released the position paper A Path Towards Autonomous Machine Intelligence — proposed configurable predictive world models, behavior driven by intrinsic motivation, and a hierarchical JEPA architecture based on self-supervised learning",
"significance": "The founding document of the JEPA and world-model approach. LLM skepticism took the form of an alternative design rather than criticism",
"source_url": "https://openreview.net/forum?id=BZ5a1r-kVsf",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0160",
"date": "2022-06-30",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "",
"actor": "Reza Yazdani Aminabadi et al. (Microsoft)",
"title": "Microsoft presents DeepSpeed Inference",
"event": "Presented DeepSpeed Inference — cut latency by up to 7.3 times and handled models 25 times larger than GPU-only setups",
"significance": "The moment a training-infrastructure team moved down into inference. The standard for serving large models in the generation before vLLM",
"source_url": "https://arxiv.org/abs/2207.00032",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0161",
"date": "2022-07",
"date_precision": "month",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "",
"actor": "Seoul National University (Gyeong-In Yu et al.)",
"title": "Orca",
"event": "Orca — iteration-level scheduling",
"significance": "The prototype of continuous batching",
"source_url": "https://www.usenix.org/conference/osdi22/presentation/yu",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0162",
"date": "2022-07-12",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "",
"actor": "Midjourney",
"title": "Midjourney launches open beta",
"event": "Launched its open beta",
"significance": "Its Discord-based UX brought in non-developer creators on a large scale",
"source_url": "https://en.wikipedia.org/wiki/Midjourney",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0163",
"date": "2022-08-15",
"date_precision": "day",
"axis": "19",
"axis_name": "On-Device & Local Inference",
"also_axes": "",
"actor": "Tim Dettmers et al.",
"title": "Dettmers et al. present LLM.int8()",
"event": "Presented LLM.int8() — converts 175B checkpoints to 8-bit without performance degradation, halving inference memory",
"significance": "Made it possible to run large models on consumer-grade GPU servers",
"source_url": "https://arxiv.org/abs/2208.07339",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0164",
"date": "2022-08-22",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "",
"actor": "Stability AI, CompVis",
"title": "Stability AI and CompVis release Stable Diffusion v1.4 weights",
"event": "Released the Stable Diffusion v1.4 weights",
"significance": "Made a high-quality generative model that runs on consumer GPUs open-weight, sparking both the LoRA and ControlNet ecosystem and the copyright debate",
"source_url": "https://stability.ai/news/stable-diffusion-public-release",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0165",
"date": "2022-09-12",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "",
"actor": "Paulius Micikevicius et al. (NVIDIA, Arm, Intel)",
"title": "NVIDIA, Arm and Intel present FP8 Formats for Deep Learning",
"event": "Presented FP8 Formats for Deep Learning — proposed two encodings, E4M3/E5M2, and claimed performance equal to 16-bit on a 175B language model",
"significance": "As a joint proposal by the three companies, it made FP8 the de facto standard format. A specification agreement that prevented format fragmentation",
"source_url": "https://arxiv.org/abs/2209.05433",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0166",
"date": "2022-09-12",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Simon Willison",
"title": "Willison coins the term prompt injection",
"event": "Named prompt injection — coined the term by structural analogy with SQL injection and characterized it not as an academic trick but as a kind of security vulnerability",
"significance": "Foundational vocabulary of the LLM security field. Every later security framework, including the OWASP LLM Top 10, uses the term",
"source_url": "https://simonwillison.net/2022/Sep/12/prompt-injection/",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0167",
"date": "2022-09-21",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "",
"actor": "OpenAI",
"title": "OpenAI open-sources Whisper",
"event": "Open-sourced Whisper — weakly supervised training on 680,000 hours of multilingual data",
"significance": "Commoditized multilingual ASR, making speech a standard input to LLM pipelines",
"source_url": "https://openai.com/index/whisper/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0168",
"date": "2022-09-21",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "Anthropic",
"title": "Toy Models of Superposition",
"event": "Toy Models of Superposition",
"significance": "Demonstrated that neurons store more features than concepts in superposition; the theoretical starting point of the entire SAE family",
"source_url": "https://arxiv.org/abs/2209.10652",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0169",
"date": "2022-10-06",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Shunyu Yao et al. (Princeton, Google Research)",
"title": "Yao et al. present ReAct",
"event": "Presented ReAct — alternates between generating a thought (a reasoning trace) and an action",
"significance": "The standard form of the agent loop that combines reasoning techniques with tool use. Cross-referenced with axis 3",
"source_url": "https://arxiv.org/abs/2210.03629",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0170",
"date": "2022-10-07",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Zhuosheng Zhang et al. (Shanghai Jiao Tong University, AWS)",
"title": "Zhang et al. present Auto-CoT",
"event": "Presented Auto-CoT — clusters questions to build CoT exemplars automatically",
"significance": "An early attempt to automate the manual work of writing CoT exemplars",
"source_url": "https://arxiv.org/abs/2210.03493",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0171",
"date": "2022-10-13",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "US Bureau of Industry and Security (BIS)",
"title": "US BIS imposes first round of advanced computing export controls",
"event": "First round of advanced computing export controls — established ECCN 3A090/4A090",
"significance": "Redefined compute as a strategic good; the prototype of every later regulation",
"source_url": "https://federalregister.gov/documents/2022/10/13/2022-21658/implementation-of-additional-export-controls-certain-advanced-computing-and-semiconductor",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0172",
"date": "2022-10-13",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "",
"actor": "Niklas Muennighoff et al. (Hugging Face, Cohere)",
"title": "Hugging Face and Cohere release MTEB",
"event": "Released MTEB — 8 tasks, 58 datasets, 112 languages",
"significance": "The common yardstick for the embedding model race. Its key conclusion: no embedding method dominates every task",
"source_url": "https://arxiv.org/abs/2210.07316",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0173",
"date": "2022-10-25",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "Harrison Chase",
"title": "Chase publishes the first public release of LangChain (PyPI 0.0.1)",
"event": "First public release of LangChain (PyPI 0.0.1)",
"significance": "Spread the standard RAG vocabulary of loader, chunking, embedding, vector store and retriever",
"source_url": "https://pypi.org/pypi/langchain/0.0.1/json",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0174",
"date": "2022-10-31",
"date_precision": "day",
"axis": "19",
"axis_name": "On-Device & Local Inference",
"also_axes": "",
"actor": "Elias Frantar et al. (IST Austria, ETH)",
"title": "Frantar et al. present GPTQ",
"event": "Presented GPTQ — post-training quantization of a 175B model to 3–4 bits in about 4 GPU hours, fitting it on a single GPU",
"significance": "Standardized quantization as a post-processing step",
"source_url": "https://arxiv.org/abs/2210.17323",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0175",
"date": "2022-11-03",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Zhou et al.",
"title": "APE — LLMs Are Human-Level Prompt Engineers",
"event": "APE — LLMs Are Human-Level Prompt Engineers",
"significance": "Automatically generated instructions matched or beat human-written ones on 19 of 24 tasks",
"source_url": "https://arxiv.org/abs/2211.01910",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0176",
"date": "2022-11-16",
"date_precision": "day",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
"also_axes": "",
"actor": "Percy Liang et al. (Stanford CRFM)",
"title": "Stanford CRFM releases HELM",
"event": "Released HELM — 42 scenarios × 7 metrics × 30 models. Raised the share of evaluation scenarios shared across models from 17.9% to 96.0%",
"significance": "The work that made comparability between models possible in the first place",
"source_url": "https://arxiv.org/abs/2211.09110",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0177",
"date": "2022-11-18",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Luyu Gao et al. (CMU)",
"title": "Gao et al. present PAL (Program-aided Language Models)",
"event": "Presented PAL (Program-aided Language Models) — generates reasoning steps as Python code and hands execution off to an interpreter",
"significance": "The lineage of moving computation outside the language model",
"source_url": "https://arxiv.org/abs/2211.10435",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0178",
"date": "2022-11-22",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Wenhu Chen et al. (University of Waterloo, Google Research)",
"title": "Chen et al. present Program of Thoughts",
"event": "Presented Program of Thoughts — separates reasoning from computation, expressing numerical reasoning as programs",
"significance": "Together with PAL, formed the code-as-reasoning lineage",
"source_url": "https://arxiv.org/abs/2211.12588",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0179",
"date": "2022-11-22",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "Jerry Liu",
"title": "Jerry Liu publishes the first public release of GPT Index (now LlamaIndex)",
"event": "First public release of GPT Index (now LlamaIndex)",
"significance": "Arriving the month after LangChain, it shaped another standard vocabulary for the RAG indexing layer",
"source_url": "https://pypi.org/pypi/gpt-index/0.0.4/json",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0180",
"date": "2022-11-25",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "Cyberspace Administration of China and others (3 ministries)",
"title": "China promulgates deep synthesis provisions for internet services (Order No. 12)",
"event": "Promulgated the Provisions on the Administration of Deep Synthesis Internet Information Services (Order No. 12) — effective Jan 10, 2023",
"significance": "Real-name authentication and labelling duties for deepfakes. The forerunner of generative AI regulation",
"source_url": "https://www.cac.gov.cn/2022-12/11/c_1672221949354811.htm",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0181",
"date": "2022-11-30",
"date_precision": "day",
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"axis_name": "Prompt Engineering",
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"actor": "OpenAI",
"title": "OpenAI releases ChatGPT as a research preview",
"event": "Released ChatGPT as a research preview",
"significance": "The conversational UI opened prompting to non-experts, turning a skill of a few practitioners into a mass phenomenon",
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{
"id": "E0182",
"date": "2022-11-30",
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"axis": "8",
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"actor": "Google (Leviathan et al.)",
"title": "Speculative Decoding",
"event": "Speculative Decoding",
"significance": "Sidesteps the memory-bound nature of decoding without changing the output distribution",
"source_url": "https://arxiv.org/abs/2211.17192",
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},
{
"id": "E0183",
"date": "2022-12-06",
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"axis": "15",
"axis_name": "Regulation, Law & Governance",
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"actor": "Council of the EU",
"title": "EU Council adopts its general approach on the AI Act",
"event": "Adopted its general approach on the AI Act — the date the Council fixed its negotiating position",
"significance": "The day the Council's position for the EU trilogue negotiations was locked in — later changes to the text were negotiated against this document",
"source_url": "https://artificialintelligenceact.eu/development/",
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{
"id": "E0184",
"date": "2022-12-07",
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"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "",
"actor": "Liang Wang et al. (Microsoft)",
"title": "Microsoft researchers present E5",
"event": "Presented E5 — contrastive training on large-scale weakly supervised pair data; claimed to be the first model to beat BM25 zero-shot on BEIR",
"significance": "A rebuttal to the challenge BEIR had raised. The starting point of the open-weight embedding race",
"source_url": "https://arxiv.org/abs/2212.03533",
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},
{
"id": "E0185",
"date": "2022-12-08",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "",
"actor": "Ilharco et al.",
"title": "Task Arithmetic",
"event": "Task Arithmetic",
"significance": "Adding and subtracting task vectors to inject or remove capabilities made model merging something you can compute",
"source_url": "https://arxiv.org/abs/2212.04089",
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{
"id": "E0186",
"date": "2022-12-15",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Anthropic (Bai et al.)",
"title": "Constitutional AI / RLAIF",
"event": "Constitutional AI / RLAIF",
"significance": "Governs behavior with written principles and AI feedback instead of human labels, elevating a principles document into a training signal",
"source_url": "https://arxiv.org/abs/2212.08073",
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"claim_type": "fact",
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{
"id": "E0187",
"date": "2022-12-15",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "",
"actor": "OpenAI",
"title": "OpenAI releases text-embedding-ada-002",
"event": "Released text-embedding-ada-002 — merged 5 models into 1, 1536 dimensions, 8192 context, price cut by 99.8% compared with davinci",
"significance": "Turned embeddings into a commodity and removed the cost barrier to the RAG boom",
"source_url": "https://openai.com/index/new-and-improved-embedding-model/",
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"claim_type": "fact",
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{
"id": "E0188",
"date": "2022-12-20",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "",
"actor": "UW, AI2 (Wang et al.)",
"title": "Self-Instruct",
"event": "Self-Instruct",
"significance": "A bootstrap in which a model creates its own training data; the starting point of the synthetic data industry",
"source_url": "https://arxiv.org/abs/2212.10560",
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},
{
"id": "E0189",
"date": "2023-01-24",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Andrej Karpathy",
"title": "Andrej Karpathy posts \"The hottest new programming language is English\" on X",
"event": "Posted \"The hottest new programming language is English\" on X",
"significance": "A one-liner that defined natural-language prompting as programming. It later became the slogan of prompt engineering discourse. The original X post was not checked directly; this relies on Quote Investigator's documentation",
"source_url": "https://quoteinvestigator.com/2024/10/20/hottest-program/",
"source_type": "secondary",
"claim_type": "statement",
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},
{
"id": "E0190",
"date": "2023-02-02",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "",
"actor": "DeepMind (Chen et al.)",
"title": "Accelerating LLM Decoding with Speculative Sampling",
"event": "Accelerating LLM Decoding with Speculative Sampling",
"significance": "An independent formulation of the same technique, with a 2–2.5× speedup",
"source_url": "https://arxiv.org/abs/2302.01318",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0191",
"date": "2023-02-05",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "",
"actor": "Rumbelow & Watkins",
"title": "SolidGoldMagikarp",
"event": "SolidGoldMagikarp — found 141 glitch tokens",
"significance": "Popularized the idea that a mismatch between the tokenizer and the model's training corpus is a real failure mode",
"source_url": "https://www.lesswrong.com/posts/aPeJE8bSo6rAFoLqg/solidgoldmagikarp-plus-prompt-generation",
"source_type": "primary",
"claim_type": "fact",
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},
{
"id": "E1213",
"date": "2023-02-06",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Google",
"title": "Google announces Bard",
"event": "Bard announced — an experimental conversational AI service powered by LaMDA, opened first to trusted testers",
"significance": "Google said it would start with a lightweight version of LaMDA to reduce compute needs and gather feedback, before wider public availability in the coming weeks",
"source_url": "https://blog.google/technology/ai/bard-google-ai-search-updates/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E0192",
"date": "2023-02-09",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Kevin Liu / Microsoft Bing",
"title": "Prompt injection leaks Bing Chat's codename Sydney and its system instructions",
"event": "Prompt injection leaked Bing Chat's internal codename Sydney and its system instructions",
"significance": "Publicly demonstrated that in a live service the system prompt does not work as a line of defense",
"source_url": "https://mspoweruser.com/chatgpt-powered-bing-discloses-original-directives-after-prompt-injection-attack-latest-microsoft-news/",
"source_type": "secondary",
"claim_type": "fact",
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},
{
"id": "E0193",
"date": "2023-02-09",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "",
"actor": "Meta AI (Schick et al.)",
"title": "Toolformer",
"event": "Toolformer",
"significance": "Learns on its own, through self-supervised learning, when to call APIs and with what arguments — the route of building tool use into the weights",
"source_url": "https://arxiv.org/abs/2302.04761",
"source_type": "primary",
"claim_type": "fact",
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},
{
"id": "E0194",
"date": "2023-02-10",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "Hugging Face",
"title": "Hugging Face ships PEFT v0.1.0, the library's first official release",
"event": "First official release of the PEFT library, v0.1.0",
"significance": "Wired up LoRA and Prefix/Prompt Tuning in a few lines, laying the groundwork for fine-tuning on consumer GPUs to go mainstream",
"source_url": "https://pypi.org/pypi/peft/json",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0195",
"date": "2023-02-24",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Meta AI",
"title": "Meta AI releases LLaMA 1 (7B/13B/33B/65B) for vetted non-commercial research",
"event": "Released LLaMA 1 (7B/13B/33B/65B) under a vetting process, for non-commercial research only",
"significance": "The first large attempt to distribute frontier-class weights after vetting",
"source_url": "https://ai.meta.com/blog/large-language-model-llama-meta-ai/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0196",
"date": "2023-03",
"date_precision": "month",
"axis": "19",
"axis_name": "On-Device & Local Inference",
"also_axes": "",
"actor": "Georgi Gerganov",
"title": "Georgi Gerganov starts developing llama.cpp",
"event": "Started developing llama.cpp — Llama inference implemented in pure C/C++ with no dependencies",
"significance": "Spread among users without dedicated hardware. Being able to run on a CPU alone was decisive",
"source_url": "https://en.wikipedia.org/wiki/Llama.cpp",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0197",
"date": "2023-03-03",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Anonymous",
"title": "LLaMA weights leak via torrent",
"event": "LLaMA weights leaked via torrent",
"significance": "Proved that controlled distribution is physically impossible; the real starting point of the open-weight ecosystem",
"source_url": "https://en.wikipedia.org/wiki/Llama_(language_model)",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0198",
"date": "2023-03-09",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Anthropic (reported by Fortune)",
"title": "Anthropic's Prompt Engineer and Librarian job reported at $175,000–$335,000",
"event": "A Prompt Engineer and Librarian job posting was reported with a salary of $175,000–$335,000",
"significance": "Prompt engineering institutionalized as a job with a title and a salary band",
"source_url": "https://fortune.com/2023/03/09/new-ai-jobs-chatgpt-like-assistants/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0199",
"date": "2023-03-10",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "",
"actor": "Georgi Gerganov",
"title": "Georgi Gerganov creates the llama.cpp repository",
"event": "Created the llama.cpp repository",
"significance": "Dependency-free inference in pure C/C++ opened the way to running LLMs on a laptop without a GPU",
"source_url": "https://api.github.com/repos/ggml-org/llama.cpp",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0200",
"date": "2023-03-13",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Stanford CRFM",
"title": "Alpaca 7B",
"event": "Alpaca 7B — self-instruct 52K, total cost under $600",
"significance": "Demonstrated that instruction-following can be replicated for a small sum",
"source_url": "https://crfm.stanford.edu/2023/03/13/alpaca.html",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0201",
"date": "2023-03-14",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Anthropic",
"title": "Anthropic launches Claude",
"event": "Launched Claude to the public",
"significance": "Released the same day as GPT-4, symbolizing that competition was moving beyond prompting tricks",
"source_url": "https://www.anthropic.com/news/introducing-claude",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0202",
"date": "2023-03-14",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "OpenAI",
"title": "OpenAI releases GPT-4",
"event": "Released GPT-4",
"significance": "Better instruction understanding reduced the marginal value of prompting tricks; the axis of competition moved to models, safety and context",
"source_url": "https://openai.com/index/gpt-4-research/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0203",
"date": "2023-03-15",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "",
"actor": "PyTorch Foundation",
"title": "PyTorch 2.0 released (torch.compile in beta)",
"event": "Released PyTorch 2.0 (torch.compile in beta) — works on 93% of 165 open-source models, 36% faster on average with AMP. Adopted Triton as the GPU backend",
"significance": "Compiler-based optimization became a framework default",
"source_url": "https://pytorch.org/blog/pytorch-2.0-release/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0204",
"date": "2023-03-20",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Noah Shinn et al. (Northeastern University, MIT)",
"title": "Shinn et al. present Reflexion",
"event": "Presented Reflexion — reflects in language on failed trajectories, stores the reflections in memory and retries",
"significance": "The lineage of verbal reinforcement learning without weight updates. The v1 title was an autonomous agent with dynamic memory and self-reflection",
"source_url": "https://arxiv.org/abs/2303.11366",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0205",
"date": "2023-03-23",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "OpenAI",
"title": "OpenAI releases ChatGPT plugins and open-sources the retrieval plugin",
"event": "Released ChatGPT plugins and open-sourced the retrieval plugin",
"significance": "The first case of a lab putting retrieval augmentation on its own product surface; a direct demand signal for the vector DB ecosystem",
"source_url": "https://openai.com/index/chatgpt-plugins/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0206",
"date": "2023-03-27",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "",
"actor": "Xiaohua Zhai et al. (Google DeepMind)",
"title": "Google DeepMind researchers present SigLIP",
"event": "Presented SigLIP — uses a pairwise sigmoid loss without softmax normalization, decoupling batch size from the loss",
"significance": "The de facto successor to CLIP in multimodal embeddings. Removing the need for very large batches greatly lowered the barrier to training",
"source_url": "https://arxiv.org/abs/2303.15343",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0207",
"date": "2023-03-28",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "",
"actor": "Yohei Nakajima",
"title": "Yohei Nakajima releases Task-Driven Autonomous Agent (later BabyAGI)",
"event": "Released Task-Driven Autonomous Agent (later BabyAGI)",
"significance": "Presented a minimal structure that runs execution, task creation and reprioritization in an endless loop",
"source_url": "https://yoheinakajima.com/task-driven-autonomous-agent-utilizing-gpt-4-pinecone-and-langchain-for-diverse-applications/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0208",
"date": "2023-03-30",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Aman Madaan et al. (CMU and others)",
"title": "Self-Refine published",
"event": "Self-Refine published — the same model repeats a generate→feedback→refine loop",
"significance": "The standard reference for iterative refinement loops without external supervision",
"source_url": "https://arxiv.org/abs/2303.17651",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0209",
"date": "2023-03-30",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "",
"actor": "Toran Bruce Richards",
"title": "Toran Bruce Richards releases AutoGPT",
"event": "AutoGPT released",
"significance": "Created the story of an AI that just gets on with it once you give it a goal, while also exposing the public to infinite loops, hallucinations and runaway costs",
"source_url": "https://en.wikipedia.org/wiki/AutoGPT",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0210",
"date": "2023-03-30",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "LMSYS",
"title": "Vicuna-13B",
"event": "Vicuna-13B — about 70,000 ShareGPT conversations, about $300",
"significance": "Established at once the practice of training on synthetic and leaked conversations and the culture of LLM-as-judge evaluation",
"source_url": "https://lmsys.org/blog/2023-03-30-vicuna/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0211",
"date": "2023-04-07",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "",
"actor": "Stanford, Google (Park et al.)",
"title": "Generative Agents",
"event": "Generative Agents",
"significance": "Emergent social behavior in a sandbox of 25 agents, produced by an observation-memory-reflection-planning architecture",
"source_url": "https://arxiv.org/abs/2304.03442",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0212",
"date": "2023-04-07",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "",
"actor": "Chroma",
"title": "Chroma announces $18 million seed round",
"event": "Announced an $18 million seed round. Positioned itself as an AI-native open-source embedding database",
"significance": "The event that created the category of embedded vector stores for LLM app developers",
"source_url": "https://www.trychroma.com/company/seed",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0213",
"date": "2023-04-17",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "",
"actor": "UW-Madison, Microsoft (Liu et al.)",
"title": "LLaVA",
"event": "LLaVA — visual instruction tuning",
"significance": "The starting point of the open VLM lineage",
"source_url": "https://arxiv.org/abs/2304.08485",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0214",
"date": "2023-04-17",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "",
"actor": "Together",
"title": "RedPajama-Data-1T",
"event": "RedPajama-Data-1T",
"significance": "An open reproduction of the LLaMA training recipe that opened an era of reverse-engineering the data behind closed models",
"source_url": "https://www.together.ai/blog/redpajama",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0215",
"date": "2023-04-21",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "",
"actor": "Weaviate",
"title": "Weaviate announces $50 million Series B",
"event": "Announced a $50 million Series B. Reported passing 2 million open-source downloads",
"significance": "A representative case of capital flowing into vector DBs at the time of the 2023 RAG boom",
"source_url": "https://www.prnewswire.com/news-releases/weaviate-raises-50-million-series-b-funding-to-meet-soaring-demand-for-ai-native-vector-database-technology-301803296.html",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0216",
"date": "2023-04-21",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "",
"actor": "Yanli Zhao et al. (Meta)",
"title": "PyTorch FSDP paper published",
"event": "PyTorch FSDP paper published — built ZeRO-style sharding into the PyTorch core",
"significance": "The turning point at which large-scale training became possible with the standard framework, without DeepSpeed",
"source_url": "https://arxiv.org/abs/2304.11277",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0217",
"date": "2023-04-26",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "Pinecone",
"title": "Pinecone raises $100 million Series B at a $750 million valuation",
"event": "Series B of $100 million, at a valuation of $750 million",
"significance": "With three large rounds in 3 weeks, the narrative of long-term memory for LLMs peaked in the capital markets",
"source_url": "https://www.pinecone.io/blog/series-b/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0218",
"date": "2023-04-28",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "",
"actor": "Stanford (Schaeffer et al.)",
"title": "Are Emergent Abilities a Mirage?",
"event": "Are Emergent Abilities a Mirage?",
"significance": "A rebuttal arguing that emergence is an artifact of discontinuous metrics; with continuous metrics the curves are smooth",
"source_url": "https://arxiv.org/abs/2304.15004",
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{
"id": "E0219",
"date": "2023-05-02",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
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"actor": "Geoffrey Hinton",
"title": "Geoffrey Hinton talks to MIT Technology Review right after leaving Google",
"event": "Interview with MIT Technology Review right after leaving Google — said he had suddenly changed his view on whether these things will become more intelligent than us, and that they are now very close. Explained that he left because he wanted to talk about AI safety without worrying about how it interacts with Google's business",
"significance": "A public change of position by one of the founders of deep learning. The turning point at which discussion of AI existential risk moved from the fringe into the mainstream press",
"source_url": "https://www.technologyreview.com/2023/05/02/1072528/geoffrey-hinton-google-why-scared-ai/",
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{
"id": "E0220",
"date": "2023-05-03",
"date_precision": "day",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
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"actor": "LMSYS",
"title": "LMSYS launches Chatbot Arena",
"event": "Chatbot Arena launched — anonymous, randomized head-to-head battles + Elo",
"significance": "Shifted the focus from benchmarks with correct answers to evaluation based on human preference",
"source_url": "https://lmsys.org/blog/2023-05-03-arena/",
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{
"id": "E0221",
"date": "2023-05-04",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
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"actor": "Reid Pryzant et al. (Microsoft)",
"title": "ProTeGi published",
"event": "ProTeGi published — automatically edits prompts using natural-language gradients and beam search",
"significance": "Early work on the idea of textual gradients. Led to TextGrad",
"source_url": "https://arxiv.org/abs/2305.03495",
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},
{
"id": "E0222",
"date": "2023-05-06",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Lei Wang et al. (Singapore Management University and others)",
"title": "Plan-and-Solve Prompting published",
"event": "Plan-and-Solve Prompting published — extends zero-shot CoT into making a plan first and then carrying it out",
"significance": "Uses a planning step to make up for zero-shot CoT's calculation and missing-step errors",
"source_url": "https://arxiv.org/abs/2305.04091",
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},
{
"id": "E0223",
"date": "2023-05-07",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Miles Turpin et al. (NYU, Cohere, Anthropic)",
"title": "Language Models Don't Always Say What They Think published",
"event": "Language Models Don't Always Say What They Think published — when biased few-shot examples are inserted, CoT produces a justification without mentioning the actual cause",
"significance": "The starting point of the line of work challenging CoT faithfulness",
"source_url": "https://arxiv.org/abs/2305.04388",
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},
{
"id": "E1214",
"date": "2023-05-10",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Google",
"title": "Google introduces PaLM 2",
"event": "PaLM 2 introduced — more heavily trained on multilingual text spanning over 100 languages, in four sizes: Gecko, Otter, Bison and Unicorn",
"significance": "At Google I/O, Google announced over 25 products and features powered by PaLM 2, including Bard, and said the smallest size, Gecko, can run on mobile devices even offline",
"source_url": "https://blog.google/technology/ai/google-palm-2-ai-large-language-model/",
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},
{
"id": "E0224",
"date": "2023-05-11",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "Anthropic",
"title": "Anthropic expands Claude's context window from 9K to 100K",
"event": "Claude's context window expanded from 9K to 100K",
"significance": "The starting gun for the long-context race; made feeding in whole documents without RAG a real option",
"source_url": "https://www.anthropic.com/news/100k-context-windows",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0225",
"date": "2023-05-17",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Shunyu Yao et al. (Princeton, Google DeepMind)",
"title": "Tree of Thoughts published",
"event": "Tree of Thoughts published — treats units of thought as nodes, with search, evaluation and backtracking",
"significance": "Redefined linear CoT as a search problem",
"source_url": "https://arxiv.org/abs/2305.10601",
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"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0226",
"date": "2023-05-22",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "",
"actor": "Ainslie et al.",
"title": "GQA",
"event": "GQA",
"significance": "A compromise between MHA and MQA that allows uptraining of existing checkpoints, and became the industry-standard KV-saving technique",
"source_url": "https://arxiv.org/abs/2305.13245",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0227",
"date": "2023-05-22",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "",
"actor": "Bo Peng et al.",
"title": "RWKV",
"event": "RWKV",
"significance": "Restructured the RNN so it can be trained at Transformer level; the opening signal of a revival of linear complexity",
"source_url": "https://arxiv.org/abs/2305.13048",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0228",
"date": "2023-05-23",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Yilun Du et al. (MIT CSAIL)",
"title": "Multiagent Debate published",
"event": "Multiagent Debate published — multiple model instances critique each other's answers and converge over several rounds",
"significance": "The reference paper for the multi-agent debate line of work",
"source_url": "https://arxiv.org/abs/2305.14325",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0229",
"date": "2023-05-23",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "Tim Dettmers et al.",
"title": "QLoRA",
"event": "QLoRA",
"significance": "Fine-tunes a 65B model on a single 48 GB GPU using 4-bit NF4, double quantization and paged optimizers",
"source_url": "https://arxiv.org/abs/2305.14314",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0230",
"date": "2023-05-24",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Shibo Hao et al. (UC San Diego and others)",
"title": "RAP (Reasoning via Planning) published",
"event": "RAP (Reasoning via Planning) published — uses the LLM as a world model and searches reasoning paths with MCTS",
"significance": "The starting point of the MCTS-based reasoning line of work",
"source_url": "https://arxiv.org/abs/2305.14992",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0231",
"date": "2023-05-29",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "Stanford (Rafailov et al.)",
"title": "DPO",
"event": "DPO",
"significance": "Optimizes directly on preference pairs without a reward model or PPO, popularizing open-source alignment",
"source_url": "https://arxiv.org/abs/2305.18290",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0232",
"date": "2023-06-01",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "",
"actor": "MIT Han Lab (Ji Lin et al.)",
"title": "AWQ",
"event": "AWQ",
"significance": "Quantization that protects only the top weights as judged by activations; TinyChat runs more than 3× faster than HF FP16",
"source_url": "https://arxiv.org/abs/2306.00978",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0233",
"date": "2023-06-01",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "",
"actor": "TII",
"title": "RefinedWeb",
"event": "RefinedWeb",
"significance": "Claimed that well-filtered web data alone can outperform curated corpora; MinHash fuzzy deduplication became a standard technique",
"source_url": "https://arxiv.org/abs/2306.01116",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0234",
"date": "2023-06-09",
"date_precision": "day",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
"also_axes": "",
"actor": "Lianmin Zheng et al. (LMSYS)",
"title": "MT-Bench and LLM-as-a-judge published",
"event": "MT-Bench and LLM-as-a-judge published — a GPT-4 judge agrees with human preferences more than 80% of the time",
"significance": "Became the basis for automated evaluation and synthetic rewards, flowing directly into post-training pipelines",
"source_url": "https://arxiv.org/abs/2306.05685",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0235",
"date": "2023-06-13",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "",
"actor": "OpenAI",
"title": "OpenAI launches function calling",
"event": "Function calling launched",
"significance": "Tool calling was promoted from prompt parsing to a structured JSON-schema interface; the de facto standard API for agent development",
"source_url": "https://openai.com/index/function-calling-and-other-api-updates/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0236",
"date": "2023-06-14",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "European Parliament",
"title": "European Parliament adopts its AI Act negotiating position",
"event": "AI Act negotiating position adopted — the date Parliament's negotiating position was finalized",
"significance": "Provisions on generative AI were added at this stage",
"source_url": "https://artificialintelligenceact.eu/development/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0237",
"date": "2023-06-16",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "",
"actor": "Guanhua Wang et al. (Microsoft)",
"title": "ZeRO++ published",
"event": "ZeRO++ published — block-quantized all-gather and quantized-gradient all-to-all cut communication volume 4×, with up to 2.16× throughput on 384 GPUs",
"significance": "An improvement that made ZeRO work even on low-bandwidth clusters",
"source_url": "https://arxiv.org/abs/2306.10209",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0238",
"date": "2023-06-20",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "",
"actor": "UC Berkeley",
"title": "UC Berkeley releases vLLM + PagedAttention",
"event": "vLLM + PagedAttention released",
"significance": "Manages the KV cache like OS paging, cutting memory waste to under 4%; the starting point of the open-source serving standard",
"source_url": "https://vllm.ai/blog/2023-06-20-vllm",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0239",
"date": "2023-06-20",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "",
"actor": "Microsoft Research",
"title": "Textbooks Are All You Need",
"event": "Textbooks Are All You Need — phi-1",
"significance": "Trained a 1.3B model on 7B tokens to reach 50.6% on HumanEval, demonstrating that data quality matters more than quantity",
"source_url": "https://arxiv.org/abs/2306.11644",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0240",
"date": "2023-06-23",
"date_precision": "day",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
"also_axes": "",
"actor": "Clémentine Fourrier et al. (Hugging Face)",
"title": "Hugging Face explains the MMLU score mismatch on the Open LLM Leaderboard",
"event": "MMLU score mismatch on the Open LLM Leaderboard explained — the same LLaMA 65B scored 0.636/0.637/0.488 depending on the implementation",
"significance": "The event that publicly confirmed benchmark numbers are not comparable",
"source_url": "https://huggingface.co/blog/evaluating-mmlu-leaderboard",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0241",
"date": "2023-07-05",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "OpenAI",
"title": "OpenAI launches Superalignment team",
"event": "Superalignment team launched — 20% of secured compute allocated over 4 years",
"significance": "Elevated oversight of superhuman AI to an explicit research program",
"source_url": "https://openai.com/index/introducing-superalignment/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0242",
"date": "2023-07-06",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "Nelson F. Liu et al. (Stanford, UC Berkeley, Samaya AI)",
"title": "Lost in the Middle published",
"event": "Lost in the Middle published — a U-shaped position bias in which performance drops when the relevant document sits in the middle of the context",
"significance": "The first large-scale counterevidence to the assumption that you can simply put in a long context",
"source_url": "https://arxiv.org/abs/2307.03172",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0243",
"date": "2023-07-10",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "Cyberspace Administration of China and others (7 agencies)",
"title": "China promulgates interim measures on generative AI services (Order No. 15)",
"event": "Interim Measures for the Management of Generative Artificial Intelligence Services promulgated (Order No. 15) — effective Aug 15, 2023",
"significance": "The world's first regulation dedicated to generative AI",
"source_url": "https://www.cac.gov.cn/2023-07/13/c_1690898327029107.htm",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1215",
"date": "2023-07-11",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Anthropic",
"title": "Anthropic releases Claude 2 and opens claude.ai in beta",
"event": "Claude 2 released — up to 100K input tokens per prompt, plus a public beta website, claude.ai, alongside the API (US and UK)",
"significance": "Company-reported scores: Codex HumanEval 71.2% (Claude 1.3: 56.0%), GSM8k 88.0% (85.2%), and 76.5% on the multiple-choice section of the Bar exam (73.0%); API priced the same as Claude 1.3",
"source_url": "https://www.anthropic.com/news/claude-2",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E0244",
"date": "2023-07-12",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "xAI",
"title": "Elon Musk publicly announces the founding of xAI",
"event": "Elon Musk publicly announced the founding of xAI. The company was founded on Mar 9, 2023, with 11 initial researchers",
"significance": "The starting point of a new frontier lab made up of people from DeepMind, OpenAI and Google. The list of founding members follows Wikipedia; an official xAI announcement has not been checked",
"source_url": "https://en.wikipedia.org/wiki/XAI_(company)",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0245",
"date": "2023-07-17",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Tamera Lanham et al. (Anthropic)",
"title": "Measuring Faithfulness in Chain-of-Thought Reasoning published",
"event": "Measuring Faithfulness in Chain-of-Thought Reasoning published — applies interventions such as inserted errors and truncated sentences to the CoT and measures how the answer changes",
"significance": "A methodology that quantified faithfulness through intervention experiments",
"source_url": "https://arxiv.org/abs/2307.13702",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0246",
"date": "2023-07-17",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "",
"actor": "Tri Dao (Princeton)",
"title": "FlashAttention-2 published",
"event": "FlashAttention-2 published — 2× FlashAttention, 50–73% of the A100's theoretical FLOPs/s, up to 225 TFLOPs/s per A100 in training",
"significance": "Halved the practical cost of long-context training. The precondition that made 32K–128K context models economically viable",
"source_url": "https://arxiv.org/abs/2307.08691",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0247",
"date": "2023-07-18",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Meta",
"title": "Meta releases Llama 2 — commercial use allowed, but under a Community License",
"event": "Llama 2 released, allowing commercial use but under a Community License",
"significance": "Popularized open weights, and the starting point of the debate over whether it is open source",
"source_url": "https://en.wikipedia.org/wiki/Llama_(language_model)",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0248",
"date": "2023-07-25",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "",
"actor": "CMU (Zhou et al.)",
"title": "WebArena",
"event": "WebArena",
"significance": "In an evaluation on real, working websites, a GPT-4 agent scored 14.41% versus 78.24% for humans, nailing down the gap between demos and reality",
"source_url": "https://arxiv.org/abs/2307.13854",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0249",
"date": "2023-07-28",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Xuefei Ning et al. (Tsinghua University, Microsoft Research)",
"title": "Skeleton-of-Thought published",
"event": "Skeleton-of-Thought published — first builds the skeleton of the answer, then decodes each point in parallel",
"significance": "A branch that applied reasoning prompting to latency optimization",
"source_url": "https://arxiv.org/abs/2307.15337",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0250",
"date": "2023-07-28",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "Google DeepMind",
"title": "RT-2",
"event": "RT-2",
"significance": "Transferred web-scale VLM knowledge directly to robot control, establishing the very category of VLA",
"source_url": "https://arxiv.org/abs/2307.15818",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0252",
"date": "2023-08",
"date_precision": "month",
"axis": "19",
"axis_name": "On-Device & Local Inference",
"also_axes": "",
"actor": "ggml project",
"title": "ggml project introduces the GGUF (GGML Universal File) format",
"event": "GGUF (GGML Universal File) format introduced — stores tensors and metadata in a single file, is mmap-compatible, and extends with backward compatibility through key-value metadata",
"significance": "The de facto standard format for deploying local LLMs",
"source_url": "https://en.wikipedia.org/wiki/Llama.cpp",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0253",
"date": "2023-08-01",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "",
"actor": "DeepWisdom (Hong et al.)",
"title": "MetaGPT",
"event": "MetaGPT",
"significance": "An assembly-line style of multi-agent system that encodes SOPs as prompt sequences",
"source_url": "https://arxiv.org/abs/2308.00352",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1216",
"date": "2023-08-03",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Alibaba Cloud",
"title": "Alibaba Cloud open-sources Qwen-7B",
"event": "Released Qwen-7B and Qwen-7B-Chat — the first open weights of the Tongyi Qianwen family, distributed on ModelScope and Hugging Face under the Tongyi Qianwen license (commercial use by application)",
"significance": "Alibaba's first open-weight language model; the original release was trained on 2.2T tokens, raised to 2.4T in an update on September 25, 2023",
"source_url": "https://github.com/QwenLM/Qwen",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E0254",
"date": "2023-08-07",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "",
"actor": "Zehan Li et al. (Alibaba)",
"title": "GTE published",
"event": "GTE published — claimed that with 110M parameters it outperforms OpenAI's embedding API and models 10× larger",
"significance": "A sign that small open-weight models were starting to overtake commercial APIs",
"source_url": "https://arxiv.org/abs/2308.03281",
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"actor": "Peng et al. (Nous, EleutherAI)",
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"actor": "Chengrun Yang et al. (Google DeepMind)",
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"actor": "Shitao Xiao, Zheng Liu et al. (BAAI)",
"title": "C-Pack released",
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"actor": "Anthropic",
"title": "Responsible Scaling Policy v1.0 (ASL system)",
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"actor": "Shehzaad Dhuliawala et al. (Meta AI, ETH Zurich)",
"title": "Chain-of-Verification published",
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"actor": "OpenAI",
"title": "OpenAI publishes GPT-4V(ision) System Card",
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"actor": "Mistral AI",
"title": "Mistral 7B",
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"actor": "Chrisantha Fernando et al. (Google DeepMind)",
"title": "Promptbreeder published",
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"actor": "MIT (Xiao et al.)",
"title": "StreamingLLM",
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"actor": "Michihiro Yasunaga et al. (Google DeepMind, Stanford)",
"title": "Analogical Prompting published",
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"actor": "Jie Huang et al. (Google DeepMind, UIUC)",
"title": "Large Language Models Cannot Self-Correct Reasoning Yet published",
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"significance": "A direct rebuttal of the Self-Refine/Reflexion family",
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"actor": "Omar Khattab et al. (Stanford, UC Berkeley and others)",
"title": "DSPy published",
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"actor": "Huaixiu Steven Zheng et al. (Google DeepMind)",
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"actor": "Carlos E. Jimenez, John Yang et al. (Princeton and others)",
"title": "SWE-bench released",
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"actor": "Charles Packer et al. (UC Berkeley)",
"title": "MemGPT published",
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"actor": "DeepMind and 21 institutions",
"title": "Open X-Embodiment / RT-X",
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"actor": "Akari Asai et al. (University of Washington, AI2, IBM Research)",
"title": "Self-RAG published",
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"actor": "BIS",
"title": "BIS issues second round of export controls",
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"source_url": "https://www.federalregister.gov/documents/2023/10/25/2023-23055/implementation-of-additional-export-controls-certain-advanced-computing-items-supercomputer-and",
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"actor": "Sainz et al.",
"title": "NLP Evaluation in trouble",
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"actor": "Houwen Peng et al. (Microsoft)",
"title": "FP8-LM published",
"event": "FP8-LM published — training GPT-175B on H100 cut actual memory use by 39% and ran 75% faster than BF16 Megatron-LM",
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"title": "G7 adopts Hiroshima Process International Guiding Principles and Code of Conduct",
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"actor": "Michael Günther et al. (Jina AI)",
"title": "Jina Embeddings 2 published",
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"significance": "Sparked the race in long-document embeddings. The first open model family to reduce the burden of chunking",
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"actor": "28 countries including the UK, plus the EU",
"title": "Bletchley Declaration adopted (AI Safety Summit)",
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"actor": "xAI",
"title": "xAI unveils Grok",
"event": "Grok unveiled — prototype Grok-0 (33B); the main model Grok-1 scored 62.9% on GSM8k, 73% on MMLU and 63.2% on HumanEval (company-reported by xAI)",
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"actor": "OpenAI",
"title": "OpenAI announces GPT-4 Turbo 128K and Assistants API Retrieval",
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"significance": "The turning point at which homemade RAG pipelines began to be absorbed into built-in platform features",
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"actor": "OpenAI",
"title": "DevDay",
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"actor": "Yang et al.",
"title": "Rethinking Benchmark and Contamination",
"event": "Rethinking Benchmark and Contamination — Rephrased Samples",
"significance": "Rephrasing or translation alone defeats n-gram decontamination, and 8–18% of HumanEval overlaps with public corpora",
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"actor": "NVIDIA",
"title": "NVIDIA announces H200",
"event": "H200 announced — 141 GB of HBM3e, 4.8 TB/s",
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"source_url": "https://nvidianews.nvidia.com/news/nvidia-supercharges-hopper-the-worlds-leading-ai-computing-platform",
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{
"id": "E0290",
"date": "2023-11-14",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "",
"actor": "João Moura",
"title": "João Moura publishes the first CrewAI release on PyPI",
"event": "First CrewAI release on PyPI",
"significance": "A structured multi-agent setup that explicitly assigns roles, goals and tasks; a shift that gives up full autonomy in favor of designing workflows",
"source_url": "https://pypi.org/pypi/crewai/0.1.0/json",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0291",
"date": "2023-11-20",
"date_precision": "day",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
"also_axes": "",
"actor": "David Rein et al.",
"title": "GPQA released",
"event": "GPQA released — 448 questions; PhD-level experts score 65%, non-experts allowed 30 minutes of web search score 34%",
"significance": "Made difficulty that searching cannot solve its design principle, linking it to scalable oversight research",
"source_url": "https://arxiv.org/abs/2311.12022",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0292",
"date": "2023-11-21",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "Anthropic",
"title": "Anthropic releases Claude 2.1 with a 200K-token context",
"event": "Claude 2.1 released, with a 200K-token context",
"significance": "The context-length race entered the 200,000-token range",
"source_url": "https://www.anthropic.com/news/claude-2-1",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0293",
"date": "2023-11-21",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "Greg Kamradt",
"title": "Greg Kamradt publishes the needle-in-a-haystack long-context recall stress test",
"event": "Needle in a haystack long-context recall stress test published",
"significance": "A community test settled in as the de facto standard evaluation, establishing the recognition that the stated context length and actual recall performance differ",
"source_url": "https://x.com/GregKamradt/status/1727018183608193393",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0294",
"date": "2023-11-21",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "",
"actor": "Meta, HuggingFace (Mialon et al.)",
"title": "GAIA benchmark",
"event": "GAIA benchmark",
"significance": "On 466 questions, humans scored 92% versus 15% for GPT-4 with plugins; shifted the focus of evaluation from hard exams to practical ability",
"source_url": "https://arxiv.org/abs/2311.12983",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0295",
"date": "2023-11-23",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Andrej Karpathy",
"title": "Andrej Karpathy posts Intro to Large Language Models on YouTube",
"event": "Intro to Large Language Models posted on YouTube — in the LLM OS section, presented an operating-system analogy that places the LLM as the kernel and tools, the file system and multimodal I/O as peripherals",
"significance": "Spread the frame of seeing the LLM not as a model but as an OS kernel, which became the shared metaphor for later discussions of agents, tool calling and context management",
"source_url": "https://archive.org/details/youtube-zjkBMFhNj_g",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0296",
"date": "2023-11-29",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "DeepMind (Nature)",
"title": "GNoME",
"event": "GNoME — 2.2 million structures, 381,000 new stable materials",
"significance": "A symbol of AI-driven materials discovery, and later the subject of the biggest reproducibility dispute",
"source_url": "https://www.nature.com/articles/s41586-023-06735-9",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1219",
"date": "2023-11-29",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "DeepSeek",
"title": "DeepSeek releases DeepSeek LLM 7B and 67B",
"event": "Released DeepSeek LLM 7B and 67B (Base and Chat) — trained from scratch on 2T tokens of English and Chinese, with intermediate training checkpoints also released; commercial use permitted under the model license",
"significance": "A general-purpose DeepSeek model released down to its intermediate checkpoints; DeepSeek claimed the 67B Base outperformed Llama 2 70B Base in reasoning, coding, math and Chinese comprehension",
"source_url": "https://github.com/deepseek-ai/DeepSeek-LLM",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E0297",
"date": "2023-11-30",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "",
"actor": "Microsoft (Patel, Choukse et al.)",
"title": "Splitwise",
"event": "Splitwise — phase splitting",
"significance": "The first proposal in the prefill-decode disaggregation line",
"source_url": "https://arxiv.org/abs/2311.18677",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0298",
"date": "2023-12-01",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "",
"actor": "Gu & Dao",
"title": "Mamba",
"event": "Mamba — input-dependent selective SSM",
"significance": "With linear-time sequence modeling, made what comes after the Transformer a serious option",
"source_url": "https://arxiv.org/abs/2312.00752",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0299",
"date": "2023-12-01",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "ASE (Calvin Shiao et al.)",
"title": "ASE describes 5 categories of manufacturing AI on its tech blog",
"event": "Described 5 categories of manufacturing AI on its own tech blog — predictive maintenance, quality assurance (object detection, segmentation, anomaly detection), process parameter optimization, RPA, supply chain optimization. No specific names of deployed systems, scale or quantitative results at all",
"significance": "The very fact that even ASE does not disclose quantitative results for back-end inspection AI is itself a benchmarking reference",
"source_url": "https://ase.aseglobal.com/blog/technology/ai-driven-innovation-for-manufacturing-automation/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0300",
"date": "2023-12-06",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "",
"actor": "Google",
"title": "Gemini 1.0",
"event": "Gemini 1.0 — natively multimodal",
"significance": "Declared an approach of pretraining all modalities together from the start, rather than attaching encoders afterward",
"source_url": "https://blog.google/technology/ai/google-gemini-ai/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0301",
"date": "2023-12-09",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "Council of the EU, European Parliament, European Commission",
"title": "AI Act trilogue reaches political agreement",
"event": "Political agreement in the AI Act trilogue — the date of the agreement, not the date the law was enacted",
"significance": "A date often wrongly cited as the passage of the AI Act",
"source_url": "https://artificialintelligenceact.eu/development/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0302",
"date": "2023-12-11",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Mistral AI",
"title": "Mixtral 8x7B",
"event": "Mixtral 8x7B — 46.7B total / 12.9B per token, Apache 2.0",
"significance": "Made Sparse MoE a standard architecture of the open ecosystem and imprinted the concept of total versus active parameters",
"source_url": "https://mistral.ai/news/mixtral-of-experts",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0303",
"date": "2023-12-12",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "",
"actor": "Zheng et al.",
"title": "SGLang paper",
"event": "SGLang paper — RadixAttention",
"significance": "Automatically reuses the prefix KV cache through a radix tree",
"source_url": "https://arxiv.org/abs/2312.07104",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0304",
"date": "2023-12-14",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "OpenAI",
"title": "Weak-to-strong generalization",
"event": "Weak-to-strong generalization",
"significance": "A scalable oversight experimental paradigm asking whether a weak supervisor can steer a strong model",
"source_url": "https://openai.com/index/weak-to-strong-generalization/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1220",
"date": "2023-12-14",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "11",
"actor": "Upstage",
"title": "Upstage releases SOLAR 10.7B",
"event": "Released SOLAR 10.7B — built by depth up-scaling (DUS) an open-source 7B model and pretrained on over 3T tokens; base model under Apache 2.0, instruction-tuned model under CC BY-NC 4.0",
"significance": "A model from a Korean startup that, per Upstage, topped the Hugging Face Open LLM Leaderboard at release; the 74.2 average belonged to the instruction-tuned SOLAR-10.7B-Instruct, while the base model scored 66.04",
"source_url": "https://www.upstage.ai/news/solar-10-7b-emerges-as-worlds-top-pre-trained-llm",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E0305",
"date": "2023-12-27",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "The New York Times",
"title": "NYT sues Microsoft and OpenAI",
"event": "NYT v. Microsoft and OpenAI filed — the filing date (SDNY)",
"significance": "The first major training-data lawsuit brought by a news organization",
"source_url": "https://en.wikipedia.org/wiki/The_New_York_Times_v._Microsoft_and_OpenAI",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0306",
"date": "2023-12-27",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "Ministry of Culture, Sports and Tourism, Korea Copyright Commission",
"title": "Korean government publishes a generative AI copyright guide",
"event": "Generative AI copyright guide published — recommended securing lawful permission to use works in advance wherever possible, even for AI training, and stated that it is unclear whether fair use applies until established case law accumulates",
"significance": "A government document itself acknowledges that Korea has no TDM exemption like Article 30-4 of Japan's Copyright Act. The document that defines the asymmetry between Korean and Japanese law on training data",
"source_url": "https://grant-documents.thevc.kr/254561_03_%EC%83%9D%EC%84%B1%ED%98%95AI+%EC%A0%80%EC%9E%91%EA%B6%8C+%EC%95%88%EB%82%B4%EC%84%9C_%EB%AC%B8%EC%B2%B4%EB%B6%80,%ED%95%9C%EA%B5%AD%EC%A0%80%EC%9E%91%EA%B6%8C%EC%9C%84%EC%9B%90%ED%9A%8C.pdf",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0307",
"date": "2023-12-31",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "",
"actor": "Sardana, Frankle et al.",
"title": "Beyond Chinchilla-Optimal",
"event": "Beyond Chinchilla-Optimal",
"significance": "Included inference cost in the objective, providing the theoretical basis for today's overtraining norm",
"source_url": "https://arxiv.org/abs/2401.00448",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0308",
"date": "2024",
"date_precision": "year",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "ASE Holdings",
"title": "ASE Holdings publishes its deployment history on its smart factory page",
"event": "Published its deployment history on its own smart factory page — AMR/AGV (2015), AI-based equipment anomaly detection (2018), predictive maintenance (2019), AI/AGV automated line inspection (2020). 56 lights-out factories as of 2024. No figures given for productivity or yield improvement rates",
"significance": "What the world's No. 1 OSAT discloses is effectively the industry ceiling. The order matters: FDC, RMS and traceability were laid down first as the prerequisite infrastructure for AI — the data pipeline comes before the inspection model",
"source_url": "https://www.aseglobal.com/csr/sustainability-governance/smart-factory",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0309",
"date": "2024-01-08",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "ISO/IEC JTC 1/SC 42",
"title": "ISO/IEC TR 5469 published",
"event": "ISO/IEC TR 5469 published — AI and functional safety. Distinguishes three types: use of AI inside a safety-related function, assuring AI-controlled equipment with non-AI means, and use of AI in the design and development of safety-related functions",
"significance": "When inspection AI is tied into a safety interlock, defining which type it falls under is the starting point for audit readiness. Note, however, that it is a Technical Report, not a normative standard that can serve as a basis for certification",
"source_url": "https://www.iso.org/standard/81283.html",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0310",
"date": "2024-01-10",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "Anthropic",
"title": "Sleeper Agents",
"event": "Sleeper Agents",
"significance": "Showed that backdoors survive standard safety training, and that adversarial training strengthens concealment instead of removing them",
"source_url": "https://arxiv.org/abs/2401.05566",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0311",
"date": "2024-01-16",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "",
"actor": "Pinecone",
"title": "Pinecone serverless announced",
"event": "Pinecone serverless announced — separates reads, writes and storage, eliminating idle compute costs",
"significance": "Shifted the cost structure of vector DBs from always-on to usage-based",
"source_url": "https://www.pinecone.io/blog/serverless/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0312",
"date": "2024-01-17",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "Trieu H. Trinh et al. (Google DeepMind)",
"title": "AlphaGeometry paper published in Nature",
"event": "AlphaGeometry paper published in Nature — a neuro-symbolic combination trained on 100 million synthetic theorems and proofs solved 25 of 30 olympiad geometry problems (previous best method: 10)",
"significance": "An early case of formally verifiable rewards driving reasoning training. Produces human-readable proofs",
"source_url": "https://www.nature.com/articles/s41586-023-06747-5",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0313",
"date": "2024-01-17",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "",
"actor": "LMSYS",
"title": "LMSYS releases SGLang + RadixAttention",
"event": "SGLang + RadixAttention released",
"significance": "Brought a cache layer for workloads with overlapping prompts, such as multi-turn and agents, down to the runtime level",
"source_url": "https://www.lmsys.org/blog/2024-01-17-sglang/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0314",
"date": "2024-01-18",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "",
"actor": "UCSD, PKU (Zhong et al.)",
"title": "DistServe",
"event": "DistServe — goodput-optimal PD disaggregation",
"significance": "Splits prefill and decode into separate GPU pools so that latency metrics can be tuned independently",
"source_url": "https://arxiv.org/abs/2401.09670",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0315",
"date": "2024-01-23",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "",
"actor": "Qdrant",
"title": "Qdrant announces $28 million Series A",
"event": "Announced a $28 million Series A. Reported passing 5 million downloads",
"significance": "Secured capital for the approach of a Rust-based, high-performance, single-purpose engine",
"source_url": "https://qdrant.tech/blog/series-a-funding-round/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0316",
"date": "2024-01-25",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "",
"actor": "OpenAI",
"title": "OpenAI releases text-embedding-3-small/large",
"event": "text-embedding-3-small/large released — large goes up to 3072 dimensions, small is priced 5× lower than its predecessor, and dimension reduction is supported through the dimensions parameter",
"significance": "MRL-style truncation became a built-in feature of a commercial API",
"source_url": "https://openai.com/index/new-embedding-models-and-api-updates/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0317",
"date": "2024-01-29",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "Shi-Qi Yan et al. (University of Science and Technology of China, UCLA)",
"title": "Corrective RAG published",
"event": "Corrective RAG published — an evaluator judges retrieval results as correct/incorrect/ambiguous and corrects them with web search",
"significance": "A defensive RAG design that assumes retrieval will fail",
"source_url": "https://arxiv.org/abs/2401.15884",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0318",
"date": "2024-01-31",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "Parth Sarthi et al. (Stanford)",
"title": "RAPTOR published",
"event": "RAPTOR published — recursively summarizes documents into a tree and retrieves at multiple levels of abstraction",
"significance": "Proposed a hierarchical summary index as an alternative to chunk-level retrieval",
"source_url": "https://arxiv.org/abs/2401.18059",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0319",
"date": "2024-01-31",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "",
"actor": "AI2",
"title": "Dolma",
"event": "Dolma — 3 trillion tokens and a curation toolkit, open-sourced",
"significance": "Released the data together with the tools, making data science reproducible",
"source_url": "https://arxiv.org/abs/2402.00159",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0320",
"date": "2024-02-02",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "",
"actor": "Ethayarajh et al.",
"title": "KTO — alignment from binary signals based on prospect theory",
"event": "KTO — alignment from binary signals based on prospect theory",
"significance": "Concluded that no loss function is universally superior and that the inductive bias that suits the setting is best",
"source_url": "https://arxiv.org/abs/2402.01306",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0321",
"date": "2024-02-02",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "",
"actor": "Zach Nussbaum et al. (Nomic AI)",
"title": "Nomic Embed published",
"event": "Nomic Embed published — a fully reproducible open-source, open-weight, open-data embedding model with an 8192 context, Apache 2.0",
"significance": "The first competitive embedding model to release even its training data. Added reproducibility as a new dimension of the embedding race",
"source_url": "https://arxiv.org/abs/2402.01613",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0322",
"date": "2024-02-05",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "",
"actor": "DeepSeek",
"title": "DeepSeek introduces GRPO (DeepSeekMath)",
"event": "GRPO introduced (DeepSeekMath)",
"significance": "Group relative policy optimization without a value function later became the de facto standard for RL in reasoning models",
"source_url": "https://arxiv.org/abs/2402.03300",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0323",
"date": "2024-02-06",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Pei Zhou et al. (USC, Google DeepMind)",
"title": "Self-Discover published",
"event": "Self-Discover published — composes atomic reasoning modules through SELECT/ADAPT/IMPLEMENT so the model generates its own task-specific reasoning structure",
"significance": "Handed the very choice of prompting technique over to the model",
"source_url": "https://arxiv.org/abs/2402.03620",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0324",
"date": "2024-02-09",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Rick Battle et al. (VMware)",
"title": "The Unreasonable Effectiveness of Eccentric Automatic Prompts published",
"event": "The Unreasonable Effectiveness of Eccentric Automatic Prompts published — an automatic prompt optimizer consistently beats hand-written positive-thinking system messages",
"significance": "Demonstrated the limits of people polishing prompts by hand. Empirical grounds for the narrative that prompt engineering is in decline",
"source_url": "https://arxiv.org/abs/2402.10949",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0325",
"date": "2024-02-09",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "UCL, NYU, Anthropic (Khan et al.)",
"title": "Debating with More Persuasive LLMs (ICML 2024 Best Paper)",
"event": "Debating with More Persuasive LLMs (ICML 2024 Best Paper)",
"significance": "The first empirical evidence that debate-based oversight actually raises the accuracy of non-expert judges",
"source_url": "https://arxiv.org/abs/2402.06782",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0326",
"date": "2024-02-13",
"date_precision": "day",
"axis": "21",
"axis_name": "Graph Engineering",
"also_axes": "2",
"actor": "Microsoft Research",
"title": "Microsoft Research publishes GraphRAG blog post",
"event": "GraphRAG blog post published — performs QA over private data using a knowledge graph generated by an LLM",
"significance": "A lineage of structured indexes beyond vector similarity search",
"source_url": "https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0327",
"date": "2024-02-15",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Xuezhi Wang et al. (Google DeepMind)",
"title": "Chain-of-Thought Reasoning Without Prompting published",
"event": "Chain-of-Thought Reasoning Without Prompting published — CoT paths emerge simply by branching on the top-k tokens during decoding instead of greedy decoding",
"significance": "Suggested that CoT may be a decoding problem rather than a prompting one",
"source_url": "https://arxiv.org/abs/2402.10200",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0328",
"date": "2024-02-15",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "Google DeepMind",
"title": "Gemini 1.5 Pro",
"event": "Gemini 1.5 Pro — 128K standard, 1 million tokens in preview",
"significance": "Context length jumped by an order of magnitude, sparking the RAG-is-dead debate",
"source_url": "https://blog.google/technology/ai/google-gemini-next-generation-model-february-2024/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0329",
"date": "2024-02-15",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "",
"actor": "OpenAI",
"title": "OpenAI announces Sora with a technical report",
"event": "Sora announced, with a technical report — diffusion transformer, spacetime patches",
"significance": "Sparked the discourse that video generation models are world simulators",
"source_url": "https://openai.com/index/video-generation-models-as-world-simulators/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0330",
"date": "2024-02-21",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Google",
"title": "Gemma 1 (2B/7B), under Gemma's own terms of use",
"event": "Gemma 1 (2B/7B), under Gemma's own terms of use",
"significance": "Big Tech entered the small open-weight field, but drew criticism that the license was not Apache",
"source_url": "https://en.wikipedia.org/wiki/Gemma_(language_model)",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0331",
"date": "2024-02-21",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Yang Zhilin (杨植麟, Moonshot AI)",
"title": "Yang Zhilin frames AI as scaling laws and long context as key to AGI",
"event": "In a Chinese-language interview, defined the essence of AI as a collection of scaling laws and said long context will be a very important point on the road to AGI. Likened large models to a new computer, defining context as its memory",
"significance": "The first systematic statement by a Chinese lab founder declaring long context a core pillar of the path to AGI. Supplied the language for the 2024 reshaping of China's LLM race into a long-context race",
"source_url": "https://news.qq.com/rain/a/20240208A05KFR00",
"source_type": "secondary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0332",
"date": "2024-02-22",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "",
"actor": "Ahmadian et al.",
"title": "RLOO",
"event": "RLOO — a return to REINFORCE, arguing that much of PPO is unnecessary",
"significance": "Representative of the line of work that simplifies RL",
"source_url": "https://arxiv.org/abs/2402.14740",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0333",
"date": "2024-02-23",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "",
"actor": "Jake Bruce et al. (Google DeepMind)",
"title": "Genie published",
"event": "Genie published — 11 billion parameters; trained only on unlabeled internet videos, it generates virtual environments that can be controlled without action labels",
"significance": "The starting point of the world-model lineage. The authors argue that at this scale it can be regarded as a foundation world model",
"source_url": "https://arxiv.org/abs/2402.15391",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1221",
"date": "2024-02-26",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "13",
"actor": "Mistral AI",
"title": "Mistral AI launches Mistral Large",
"event": "Launched Mistral Large — a flagship text model with 32K context, native fluency in English, French, Spanish, German and Italian, and function calling; offered via API on la Plateforme and Microsoft Azure, with no public weights",
"significance": "An API flagship launched with Microsoft Azure as Mistral's first distribution partner; Mistral claimed it ranked second only to GPT-4 among models available through an API",
"source_url": "https://mistral.ai/news/mistral-large",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E0334",
"date": "2024-03-04",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "",
"actor": "Microsoft Research India (Agrawal et al.)",
"title": "Sarathi-Serve",
"event": "Sarathi-Serve — chunked prefill, stall-free scheduling",
"significance": "Solved the problem of long prompts stalling decode; 2.6–5.6× the serving capacity of vLLM",
"source_url": "https://arxiv.org/abs/2403.02310",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1222",
"date": "2024-03-04",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "7",
"actor": "Anthropic",
"title": "Anthropic announces the Claude 3 family (Haiku, Sonnet, Opus)",
"event": "Claude 3 model family (Haiku, Sonnet, Opus) announced — three tiers, vision input, 200K context",
"significance": "Opus and Sonnet available on launch day in claude.ai and the Claude API, generally available in 159 countries, with Haiku to follow. Per 1M tokens: Opus $15/$75, Sonnet $3/$15, Haiku $0.25/$1.25; assessed at ASL-2 under the RSP",
"source_url": "https://www.anthropic.com/news/claude-3-family",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E0335",
"date": "2024-03-06",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "IEEE Spectrum (Dina Genkina)",
"title": "IEEE Spectrum publishes AI Prompt Engineering Is Dead",
"event": "Article AI Prompt Engineering Is Dead published — citing research from VMware and Intel Labs, it predicted that hand-crafted prompting would be automated and absorbed into LLMOps",
"significance": "An early record, in edited media, of the narrative that the prompt engineer job is in decline",
"source_url": "https://spectrum.ieee.org/prompt-engineering-is-dead",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0336",
"date": "2024-03-07",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "Government of India (Union Cabinet)",
"title": "India's Union Cabinet approves the IndiaAI Mission",
"event": "IndiaAI Mission approved — a budget of 103.7 billion rupees; 7 pillars, including AI compute infrastructure of at least 10,000 GPUs",
"significance": "An early case of the compute-as-public-good model, in which the state procures and allocates GPUs directly. Later a reference point for Korean and Japanese plans",
"source_url": "https://www.pib.gov.in/PressReleaseIframePage.aspx?PRID=2012355",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0337",
"date": "2024-03-12",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "",
"actor": "Cognition",
"title": "Cognition unveils Devin, billed as the first AI software engineer",
"event": "Devin unveiled, billed as the first AI software engineer",
"significance": "Announced 13.86% on SWE-bench, saying it beat the previous SOTA of 1.96% by more than 7×",
"source_url": "https://cognition.com/blog/introducing-devin",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0338",
"date": "2024-03-12",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "",
"actor": "Hong et al.",
"title": "ORPO",
"event": "ORPO — folds a preference penalty into SFT; no reference model needed",
"significance": "A further simplification of the alignment pipeline",
"source_url": "https://arxiv.org/abs/2403.07691",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0339",
"date": "2024-03-13",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "European Parliament",
"title": "AI Act adopted in final plenary vote (523 for, 46 against, 49 abstentions)",
"event": "AI Act adopted in final plenary vote (523 for, 46 against, 49 abstentions) — the date Parliament's procedure ended",
"significance": "Not yet in force",
"source_url": "https://www.europarl.europa.eu/news/en/press-room/20240308IPR19015/artificial-intelligence-act-meps-adopt-landmark-law",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0340",
"date": "2024-03-15",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "Copyright Subdivision of the Council for Cultural Affairs, Agency for Cultural Affairs (Japan)",
"title": "Japan's copyright council finalizes its views on AI and copyright",
"event": "Views on AI and copyright finalized — Article 30-4 of the Copyright Act applies only to non-enjoyment purposes, excluding cases where an enjoyment purpose coexists. Intentional overfitting, and additional training aimed at generating outputs similar to a group of works sharing a particular creator's creative expression, fall outside the exemption",
"significance": "The government drew, in writing, the actual limits of the provision that is cited internationally as making AI training free in Japan. Fine-tuning aimed at imitating an artistic style is not exempt",
"source_url": "https://www.bunka.go.jp/seisaku/bunkashingikai/chosakuken/pdf/94057901_01.pdf",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0341",
"date": "2024-03-17",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "xAI",
"title": "xAI releases Grok-1 base model weights and architecture under Apache 2.0",
"event": "Grok-1 base model weights and architecture released under Apache 2.0 — 314B MoE, 25% active per token, in-house JAX+Rust stack",
"significance": "The largest Apache 2.0 open-weight MoE at the time. But as a raw checkpoint without fine-tuning for dialogue, its practical usability was limited",
"source_url": "https://x.ai/news/grok-os",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0342",
"date": "2024-03-18",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "",
"actor": "NVIDIA",
"title": "Blackwell platform",
"event": "Blackwell platform — second-generation Transformer Engine, FP4, GB200 NVL72",
"significance": "A paradigm that treats a whole rack as one giant GPU",
"source_url": "https://nvidianews.nvidia.com/news/nvidia-blackwell-platform-arrives-to-power-a-new-era-of-computing",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0343",
"date": "2024-03-21",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "",
"actor": "Sakana AI",
"title": "Sakana AI announces Evolutionary Model Merge, releases EvoLLM-JP and EvoVLM-JP",
"event": "Announced Evolutionary Model Merge, which uses evolutionary algorithms to merge open-source models automatically, and released EvoLLM-JP and EvoVLM-JP. Published in Nature Machine Intelligence on Jan 28, 2025",
"significance": "Showed a path to non-English capability by combining existing open weights without pretraining — the prototype strategy for countries at a compute disadvantage",
"source_url": "https://sakana.ai/evolutionary-model-merge-jp/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0344",
"date": "2024-03-25",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "NTT",
"title": "NTT starts accepting applications for its in-house LLM tsuzumi",
"event": "Began accepting applications for the commercial service of its in-house LLM tsuzumi — a lightweight, Japanese-specialized approach",
"significance": "The starting point of a Japanese telecom approach that, instead of following the parameter-count race, set its course toward lightweight models specialized in the national language",
"source_url": "https://www.ntt.com/about-us/press-releases/news/article/2024/0325_2.html",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0345",
"date": "2024-03-28",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "xAI",
"title": "xAI announces Grok-1.5",
"event": "Grok-1.5 announced — 128,000-token context, 50.6% on MATH and 90% on GSM8K (company-reported by xAI)",
"significance": "Expanded the context 16× over the previous version",
"source_url": "https://x.ai/news/grok-1.5",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0346",
"date": "2024-04-01",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "",
"actor": "Gerstgrasser et al.",
"title": "Is Model Collapse Inevitable?",
"event": "Is Model Collapse Inevitable?",
"significance": "Proved that replacing real data leads to collapse but accumulating it keeps a finite upper bound on error, redefining the model-collapse discourse",
"source_url": "https://arxiv.org/abs/2404.01413",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0347",
"date": "2024-04-08",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "Cheetham & Seshadri",
"title": "Cheetham & Seshadri rebut GNoME",
"event": "GNoME rebutted — scant evidence of compounds that meet the three criteria of novelty, credibility and utility",
"significance": "Established the field's central issue: the gap between generated candidates and confirmed materials",
"source_url": "https://www.theregister.com/2024/04/11/google_deepmind_material_study/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0348",
"date": "2024-04-09",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "Cheng-Ping Hsieh et al. (NVIDIA)",
"title": "RULER published",
"event": "RULER published — goes beyond needle-in-a-haystack to measure effective context length with multi-needle, tracing and aggregation tasks",
"significance": "Exposed, in a standardized way, the gap between advertised and effective context length",
"source_url": "https://arxiv.org/abs/2404.06654",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0349",
"date": "2024-04-09",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "",
"actor": "OpenAI",
"title": "OpenAI ends the ChatGPT plugins beta, replacing it with GPTs",
"event": "ChatGPT plugins beta ended, replaced by GPTs",
"significance": "The first large-scale tool ecosystem, folded after just one year; a sign that schema-based function calling outlasts a loose marketplace",
"source_url": "https://help.openai.com/en/articles/8988022-winding-down-the-chatgpt-plugins-beta",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0350",
"date": "2024-04-09",
"date_precision": "day",
"axis": "21",
"axis_name": "Graph Engineering",
"also_axes": "2",
"actor": "Neo4j",
"title": "Neo4j launches GraphRAG for GenAI via native Google Cloud integration",
"event": "Announced the launch of GraphRAG capabilities for GenAI through native Google Cloud integration — claimed knowledge graphs reduce hallucinations and ground LLMs in facts",
"significance": "An early case of a graph DB vendor adopting GraphRAG as a product category — the point at which an academic term entered the commercial stack",
"source_url": "https://neo4j.com/press-releases/neo4j-google-cloud-graphrag-for-genai/",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0351",
"date": "2024-04-11",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "",
"actor": "Tianbao Xie et al. (University of Hong Kong, Salesforce Research and others)",
"title": "OSWorld published",
"event": "OSWorld published — a multimodal agent benchmark with execution-based evaluation in real OS environments",
"significance": "The de facto standard benchmark for computer-use agents",
"source_url": "https://arxiv.org/abs/2404.07972",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0352",
"date": "2024-04-11",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "",
"actor": "Cohere",
"title": "Cohere launches Rerank 3",
"event": "Rerank 3 launched",
"significance": "The point at which rerankers were commercialized as a separate foundation-model product line. The two-stage structure settled in as the default architecture for RAG",
"source_url": "https://cohere.com/blog/rerank-3",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0353",
"date": "2024-04-12",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "",
"actor": "Internet of Bugs (Carl Brown)",
"title": "Controversy spreads over whether Devin's Upwork demo holds up",
"event": "Controversy spread over whether Devin's Upwork demo held up under scrutiny",
"significance": "Put the reproducibility and checking of agent demos on the industry agenda",
"source_url": "https://news.ycombinator.com/item?id=40008109",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0354",
"date": "2024-04-12",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "",
"actor": "xAI",
"title": "xAI previews Grok-1.5V",
"event": "Grok-1.5V preview — its first multimodal model. Released the RealWorldQA benchmark for evaluating real-world spatial understanding at the same time",
"significance": "A case of building its own benchmark and releasing it alongside. The announcement said only that it would soon be available to early testers, so whether it was actually released to the general public has not been checked",
"source_url": "https://x.ai/news/grok-1.5v",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1223",
"date": "2024-04-17",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Mistral AI",
"title": "Mistral AI releases Mixtral 8x22B",
"event": "Released Mixtral 8x22B — a sparse MoE with 141B total / 39B active parameters, 64K context and native function calling, under Apache 2.0",
"significance": "Mistral's larger MoE follow-up to Mixtral 8x7B, again under Apache 2.0",
"source_url": "https://mistral.ai/news/mixtral-8x22b",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E0355",
"date": "2024-04-18",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Meta",
"title": "Llama 3 (8B/70B, about 15T tokens)",
"event": "Llama 3 (8B/70B, about 15T tokens)",
"significance": "The 8B delivered the previous generation's 70B-class performance, raising the bar for practical local use",
"source_url": "https://en.wikipedia.org/wiki/Llama_(language_model)",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0356",
"date": "2024-04-19",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "Ministry of Internal Affairs and Communications, Ministry of Economy, Trade and Industry (Japan)",
"title": "Japan publishes AI Guidelines for Business Ver1.0",
"event": "AI Guidelines for Business Ver1.0 published — consolidated 3 existing guidelines and stated that it will be updated as needed as a living document. 10 guiding principles: human-centricity, safety, fairness, privacy, security, transparency, accountability, education, fair competition and innovation",
"significance": "Chose a single soft-law document, in contrast to the hard law of the EU AI Act. Later became the skeleton of the AI Promotion Act",
"source_url": "https://www.mext.go.jp/content/20240725-mxt_jogai01-000037149_022.pdf",
"source_type": "primary",
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"actor": "Hugging Face",
"title": "Hugging Face releases FineWeb",
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"actor": "Microsoft Research",
"title": "Microsoft Research publishes Phi-3 technical report",
"event": "Phi-3 technical report published — phi-3-mini, 3.8B / 3.3T tokens; claimed Mixtral 8x7B-level performance with 69% on MMLU",
"significance": "Justified the approach of small models deployable on phones through data curation",
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"actor": "Jacob Pfau et al. (NYU)",
"title": "Let's Think Dot by Dot published",
"event": "Let's Think Dot by Dot published — performance on some tasks rises even with meaningless filler tokens alone",
"significance": "A rebuttal showing that CoT performance gains are not necessarily due to the content of the reasoning",
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"actor": "Darren Edge et al. (Microsoft Research)",
"title": "GraphRAG paper published",
"event": "GraphRAG paper published — detects communities in an entity graph and combines community summaries via map-reduce to answer global queries",
"significance": "Handles global summarization queries that local retrieval cannot answer",
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"actor": "GitHub",
"title": "GitHub launches Copilot Workspace technical preview",
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"significance": "With a flow running from issue to plan, code and validation, the product shifted from autocomplete to task-level agents",
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"actor": "Hugh Zhang et al. (Scale AI)",
"title": "GSM1k released",
"event": "GSM1k released — a check of GSM8k contamination. Performance drops by up to 8% on the new problem set",
"significance": "Measured benchmark contamination with a controlled experiment. However, reported that frontier models show few signs of overfitting",
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{
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"axis_name": "Tools & Agents",
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"actor": "Princeton (John Yang et al.)",
"title": "SWE-agent",
"event": "SWE-agent — the ACI (Agent-Computer Interface) concept",
"significance": "Introduced the concept of designing interfaces specifically for agents, and reached 12.5% pass@1 on SWE-bench as open source",
"source_url": "https://arxiv.org/abs/2405.15793",
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{
"id": "E0364",
"date": "2024-05-07",
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"actor": "DeepSeek-AI",
"title": "DeepSeek-V2",
"event": "DeepSeek-V2 — MLA (Multi-head Latent Attention)",
"significance": "A third path, distinct from MQA and GQA, that compresses KV into a low-dimensional latent",
"source_url": "https://arxiv.org/abs/2405.04434",
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{
"id": "E0365",
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"actor": "OpenAI",
"title": "OpenAI publishes the first Model Spec",
"event": "Model Spec published for the first time",
"significance": "Fixed the norms for model behavior in a public document, which later became an industry-standard format",
"source_url": "https://openai.com/index/introducing-the-model-spec/",
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{
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"actor": "DeepMind, Isomorphic (Nature)",
"title": "AlphaFold 3",
"event": "AlphaFold 3",
"significance": "Extended beyond proteins to complexes with nucleic acids, small molecules and ions",
"source_url": "https://www.nature.com/articles/s41586-024-07487-w",
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{
"id": "E0367",
"date": "2024-05-08",
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"axis_name": "Embeddings & Vector Search",
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"actor": "Luke Merrick et al. (Snowflake)",
"title": "Arctic-Embed released",
"event": "Arctic-Embed released — 5 models from 22M to 334M under Apache 2.0, claiming SOTA for their size on MTEB Retrieval at launch",
"significance": "A sign that data warehouse vendors were starting to build embedding models themselves",
"source_url": "https://arxiv.org/abs/2405.05374",
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{
"id": "E0368",
"date": "2024-05-13",
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"axis": "7",
"axis_name": "Multimodal",
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"actor": "OpenAI",
"title": "GPT-4o",
"event": "GPT-4o — a single end-to-end neural network across text, vision and audio",
"significance": "Voice responses in as little as 232 ms and 320 ms on average, structurally eliminating the latency and information loss of cascaded pipelines",
"source_url": "https://openai.com/index/hello-gpt-4o/",
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{
"id": "E0369",
"date": "2024-05-17",
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"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
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"actor": "Google DeepMind",
"title": "Frontier Safety Framework v1",
"event": "Frontier Safety Framework v1",
"significance": "Together with RSP and PF, established a system of three major risk-evaluation frameworks",
"source_url": "https://deepmind.google/blog/introducing-the-frontier-safety-framework/",
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{
"id": "E0370",
"date": "2024-05-17",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
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"actor": "Governor of Colorado",
"title": "SB 24-205 (Colorado AI Act) signed",
"event": "SB 24-205 (Colorado AI Act) signed — imposes a duty of reasonable care to prevent algorithmic discrimination on developers and deployers of high-risk AI; obligations originally set to apply from Feb 1, 2026",
"significance": "Widely cited as the first comprehensive state AI law in the US, but its obligations were never enforced: SB 25B-004 (signed Aug 28, 2025) pushed the date to June 30, 2026, and SB 26-189 (signed May 14, 2026) repealed and re-enacted the provisions as automated decision-making technology (ADMT) rules applying from Jan 1, 2027",
"source_url": "https://leg.colorado.gov/bills/sb24-205",
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{
"id": "E0371",
"date": "2024-05-20",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
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"actor": "Berkeley and others",
"title": "Octo",
"event": "Octo — an open-source generalist robot policy",
"significance": "The beginning of open-source VLAs",
"source_url": "https://arxiv.org/abs/2405.12213",
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},
{
"id": "E0372",
"date": "2024-05-21",
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"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
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"actor": "Anthropic",
"title": "Scaling Monosemanticity / Golden Gate Claude",
"event": "Scaling Monosemanticity / Golden Gate Claude",
"significance": "First applied SAEs to a production-grade model and demonstrated feature steering; interpretability moved from observation to intervention",
"source_url": "https://transformer-circuits.pub/2024/scaling-monosemanticity/",
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{
"id": "E0373",
"date": "2024-05-21",
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"axis": "15",
"axis_name": "Regulation, Law & Governance",
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"actor": "Council of the EU",
"title": "Council of the EU gives final approval to the AI Act",
"event": "Final approval of the AI Act by the Council — the date the legislative procedure was completed",
"significance": "The day the AI Act's legislative procedure ended. Everything after this is a question of entry-into-force and application dates, not of whether it becomes law",
"source_url": "https://artificialintelligenceact.eu/development/",
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},
{
"id": "E0374",
"date": "2024-05-21",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
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"actor": "Republic of Korea, United Kingdom",
"title": "Seoul Declaration adopted (AI Seoul Summit)",
"event": "Seoul Declaration adopted (AI Seoul Summit) — agreement to create a network of AI safety institutes",
"significance": "An international AI governance milestone co-hosted by Korea",
"source_url": "https://www.gov.uk/government/publications/seoul-declaration-for-safe-innovative-and-inclusive-ai-ai-seoul-summit-2024/seoul-declaration-for-safe-innovative-and-inclusive-ai-by-participants-attending-the-leaders-session-ai-seoul-summit-21-may-2024",
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{
"id": "E0375",
"date": "2024-05-21",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
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"actor": "20 companies",
"title": "Frontier AI Safety Commitments",
"event": "Frontier AI Safety Commitments — voluntary commitments by companies. Pledged to publish safety frameworks for severe risks by the France summit",
"significance": "The origin of the practice of each lab publishing its safety framework",
"source_url": "https://www.gov.uk/government/publications/frontier-ai-safety-commitments-ai-seoul-summit-2024/frontier-ai-safety-commitments-ai-seoul-summit-2024",
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{
"id": "E0376",
"date": "2024-05-23",
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"axis": "12",
"axis_name": "Training Data & Post-Training",
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"actor": "Meng et al.",
"title": "SimPO",
"event": "SimPO — average log probability as the implicit reward; reference-free",
"significance": "The last simplification in the direct alignment family",
"source_url": "https://arxiv.org/abs/2405.14734",
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{
"id": "E0377",
"date": "2024-05-23",
"date_precision": "day",
"axis": "21",
"axis_name": "Graph Engineering",
"also_axes": "2",
"actor": "Bernal Jiménez Gutiérrez et al. (Ohio State University, Stanford)",
"title": "HippoRAG published",
"event": "HippoRAG published — integrates multiple hops in a single retrieval step using a knowledge graph and personalized PageRank",
"significance": "The multi-hop retrieval branch of the GraphRAG lineage",
"source_url": "https://arxiv.org/abs/2405.14831",
"source_type": "primary",
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"added_week": "2026-W35"
},
{
"id": "E0378",
"date": "2024-05-26",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
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"actor": "xAI",
"title": "xAI closes $6 billion Series B",
"event": "Closed a $6 billion Series B. Participants included Valor, Vy Capital, a16z, Sequoia and Fidelity",
"significance": "The valuation was not disclosed in the announcement",
"source_url": "https://x.ai/news/series-b",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0379",
"date": "2024-05-29",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "",
"actor": "Joonhyung Lee et al. (NAVER Cloud)",
"title": "To FP8 and Back Again published",
"event": "To FP8 and Back Again published — argued that current FP8 training methods lack stability across seeds, learning rates and datasets, and so are not robust enough to be an economical substitute",
"significance": "A head-on rebuttal of the FP8 training narrative. Documented that reports that it works coexist with reports that it cannot be reproduced",
"source_url": "https://arxiv.org/abs/2405.18710",
"source_type": "primary",
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},
{
"id": "E1224",
"date": "2024-05-29",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
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"actor": "Mistral AI",
"title": "Mistral AI releases Codestral",
"event": "Released Codestral — a 22B code model covering 80+ programming languages, with 32K context and fill-in-the-middle, under the new Mistral AI Non-Production License",
"significance": "Introduced a non-production license that publishes weights for research and testing only, with commercial use requiring a separate license",
"source_url": "https://mistral.ai/news/codestral",
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{
"id": "E0380",
"date": "2024-05-30",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
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"actor": "Anthropic",
"title": "Anthropic makes Claude tool use generally available (GA)",
"event": "Claude tool use made generally available (GA)",
"significance": "Tool calling became standardized as a built-in feature across the major model providers",
"source_url": "https://claude.com/blog/tool-use-ga",
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"claim_type": "fact",
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},
{
"id": "E0381",
"date": "2024-06",
"date_precision": "month",
"axis": "20",
"axis_name": "People & Discourse",
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"actor": "Leopold Aschenbrenner",
"title": "SITUATIONAL AWARENESS",
"event": "SITUATIONAL AWARENESS: The Decade Ahead self-published — argued that AGI by 2027 is strikingly plausible, laid out a trajectory toward trillion-dollar clusters, and predicted national-security intervention in 2027–2028",
"significance": "Pushed the AGI-timeline debate onto the policy and security agenda. Introducing the frame of AGI nationalization mattered more than the predictions themselves, and it was also the most intensely rebutted document",
"source_url": "https://situational-awareness.ai/",
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"claim_type": "statement",
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},
{
"id": "E0382",
"date": "2024-06-06",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
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"actor": "Sander Schulhoff et al. (University of Maryland and others)",
"title": "The Prompt Report published",
"event": "The Prompt Report published — a large-scale survey that systematically organizes prompting techniques and terminology",
"significance": "Fixed the scattered lineages of techniques into a single taxonomy. The v1 title was Survey of Prompting Techniques",
"source_url": "https://arxiv.org/abs/2406.06608",
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"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0383",
"date": "2024-06-06",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
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"actor": "Epoch AI",
"title": "Will we run out of data?",
"event": "Will we run out of data? — about 300 trillion tokens of public human-generated text, estimated to be exhausted in 2026–2032",
"significance": "The quantitative reference point for the data-exhaustion discourse",
"source_url": "https://epoch.ai/blog/will-we-run-out-of-data-limits-of-llm-scaling-based-on-human-generated-data",
"source_type": "primary",
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{
"id": "E1225",
"date": "2024-06-07",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
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"actor": "Alibaba Qwen",
"title": "Alibaba releases Qwen2",
"event": "Released Qwen2 — five sizes (0.5B, 1.5B, 7B, 57B-A14B, 72B), trained on 27 additional languages beyond English and Chinese, with up to 128K context for the 7B and 72B Instruct models",
"significance": "Only the 72B kept the original Qianwen License while the other four moved to Apache 2.0, the release where Qwen's licensing began to open up",
"source_url": "https://qwenlm.github.io/blog/qwen2/",
"source_type": "primary",
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"added_week": "2026-W41"
},
{
"id": "E0384",
"date": "2024-06-10",
"date_precision": "day",
"axis": "19",
"axis_name": "On-Device & Local Inference",
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"actor": "Apple",
"title": "Apple unveils the Apple Intelligence on-device foundation model",
"event": "Apple Intelligence on-device foundation model unveiled — about 3 billion parameters, 3.7 bpw on average, TTFT of about 0.6 ms/token and 30 tokens per second on iPhone 15 Pro. Adapters loaded dynamically",
"significance": "The first large-scale case of OS-level on-device LLM integration",
"source_url": "https://machinelearning.apple.com/research/introducing-apple-foundation-models",
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{
"id": "E0385",
"date": "2024-06-11",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
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"actor": "Mert Yuksekgonul et al. (Stanford)",
"title": "TextGrad published",
"event": "TextGrad published — backpropagates natural-language feedback as gradients to optimize prompts, code, molecules and treatment plans",
"significance": "Extended the automatic-differentiation metaphor to text systems in general",
"source_url": "https://arxiv.org/abs/2406.07496",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0386",
"date": "2024-06-12",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
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"actor": "Pearce & Song",
"title": "Reconciling Kaplan and Chinchilla Scaling Laws",
"event": "Reconciling Kaplan and Chinchilla Scaling Laws",
"significance": "Showed that the conflict between the two laws stems from counting non-embedding parameters and from analysis at small scale",
"source_url": "https://arxiv.org/abs/2406.12907",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0387",
"date": "2024-06-12",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "",
"actor": "Xu et al.",
"title": "Magpie",
"event": "Magpie — 4 million instructions generated from nothing but an empty chat template",
"significance": "Distillation became so cheap that it no longer even needs API calls",
"source_url": "https://arxiv.org/abs/2406.08464",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0388",
"date": "2024-06-13",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
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"actor": "Maxime Labonne",
"title": "Maxime Labonne publishes Abliteration",
"event": "Abliteration published — orthogonalizing the refusal direction",
"significance": "Publicly demonstrated that safety alignment can be undone without retraining; technical grounds for the open-weight safety debate",
"source_url": "https://huggingface.co/blog/mlabonne/abliteration",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0389",
"date": "2024-06-13",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
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"actor": "Stanford and others",
"title": "OpenVLA",
"event": "OpenVLA — 7B, 970,000 real-robot demonstrations, LoRA fine-tuning",
"significance": "Lowered the barrier to entry for academia and small and medium-sized companies",
"source_url": "https://arxiv.org/abs/2406.09246",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0390",
"date": "2024-06-14",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
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"actor": "SB Intuitions (SoftBank)",
"title": "SB Intuitions releases Sarashina1 series 7B/13B/65B Japanese LLMs",
"event": "Sarashina1 series 7B/13B/65B Japanese LLMs released — stated explicitly that these are pretrained models without instruction tuning and are not intended for use as-is in practical services",
"significance": "An early case of a major Japanese company publicly releasing a pretrained model in its own language, and a sign of telecom capital entering homegrown LLMs",
"source_url": "https://www.sbintuitions.co.jp/news/press/20240614_01/",
"source_type": "primary",
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"added_week": "2026-W35"
},
{
"id": "E0391",
"date": "2024-06-17",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
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"actor": "Krista Opsahl-Ong et al. (Stanford, UC Berkeley and others)",
"title": "MIPRO published",
"event": "MIPRO published — jointly optimizes the instructions and demonstrations of multi-stage LM programs",
"significance": "The theoretical basis of DSPy's optimizers",
"source_url": "https://arxiv.org/abs/2406.11695",
"source_type": "primary",
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"added_week": "2026-W35"
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{
"id": "E0392",
"date": "2024-06-17",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
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"actor": "Shunyu Yao et al. (Sierra)",
"title": "τ-bench published",
"event": "τ-bench published — a tool-agent benchmark in which the agent interacts with a user simulator while following domain rules",
"significance": "Measures policy compliance over multiple turns rather than one-off tool calls",
"source_url": "https://arxiv.org/abs/2406.12045",
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},
{
"id": "E0393",
"date": "2024-06-17",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "",
"actor": "NVIDIA",
"title": "Nemotron-4 340B",
"event": "Nemotron-4 340B — more than 98% of the alignment data is synthetic",
"significance": "The data-generation pipeline itself became the product",
"source_url": "https://arxiv.org/abs/2406.11704",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1226",
"date": "2024-06-17",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "DeepSeek",
"title": "DeepSeek releases DeepSeek-Coder-V2",
"event": "Released DeepSeek-Coder-V2 — 236B (21B active) and 16B (2.4B active) MoE models, further pretrained from a DeepSeek-V2 intermediate checkpoint on 6T additional tokens; programming languages expanded from 86 to 338 and context from 16K to 128K",
"significance": "An open-weight code model that DeepSeek claimed matched GPT-4 Turbo on code-specific tasks",
"source_url": "https://github.com/deepseek-ai/DeepSeek-Coder-V2",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E0394",
"date": "2024-06-19",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Ilya Sutskever, Daniel Gross, Daniel Levy",
"title": "Safe Superintelligence Inc. founded",
"event": "Safe Superintelligence Inc. founded — billed itself as the world's first straight-shot SSI lab, with one goal and one product",
"significance": "The organizational form itself — heading straight for superintelligence without shipping products — is an argument. Sparked a debate over whether safety can be separated from commercial pressure",
"source_url": "https://ssi.inc/",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E1227",
"date": "2024-06-20",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "3",
"actor": "Anthropic",
"title": "Anthropic releases Claude 3.5 Sonnet",
"event": "Claude 3.5 Sonnet released — the first Claude 3.5 model, $3/$15 per 1M tokens with a 200K context window, launched alongside Artifacts",
"significance": "Anthropic said it outperforms Claude 3 Opus at twice its speed and at mid-tier Sonnet pricing; on an internal agentic coding evaluation it solved 64% of problems (Claude 3 Opus: 38%). The UK AISI ran a pre-deployment safety evaluation, and Anthropic's red teaming concluded it remains at ASL-2",
"source_url": "https://www.anthropic.com/news/claude-3-5-sonnet",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E0395",
"date": "2024-06-21",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "Ziyan Jiang et al. (University of Waterloo)",
"title": "LongRAG published",
"event": "LongRAG published — uses large retrieval units to lighten the retriever's load and hands them to a long-context reader",
"significance": "Treats RAG and long context not as rivals but as a redistribution of roles",
"source_url": "https://arxiv.org/abs/2406.15319",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0396",
"date": "2024-06-24",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "",
"actor": "Moonshot AI, Tsinghua (Qin et al.)",
"title": "Mooncake",
"event": "Mooncake — a KVCache-centric disaggregated architecture",
"significance": "Tiered KV cache management grounded in a production service",
"source_url": "https://arxiv.org/abs/2407.00079",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0397",
"date": "2024-06-25",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "",
"actor": "Hugging Face",
"title": "FineWeb paper and FineWeb-Edu, 1.3 trillion tokens",
"event": "FineWeb paper and FineWeb-Edu, 1.3 trillion tokens",
"significance": "A subset scored for educational value by an LLM classifier produced better models than the original, standardizing model-based filtering",
"source_url": "https://arxiv.org/abs/2406.17557",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0398",
"date": "2024-06-27",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "Manuel Faysse et al. (Illuin Technology, CentraleSupélec and others)",
"title": "ColPali published",
"event": "ColPali published — embeds document page images directly with a VLM and retrieves via late interaction",
"significance": "A lineage of visual document retrieval that skips the OCR and parsing pipeline",
"source_url": "https://arxiv.org/abs/2407.01449",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0399",
"date": "2024-06-27",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "Google",
"title": "Google opens Gemini 1.5 Pro's 2 million-token context to all developers",
"event": "Gemini 1.5 Pro's 2 million-token context opened to all developers",
"significance": "Ultra-long context left the waitlist and became an option for ordinary developers",
"source_url": "https://developers.googleblog.com/en/new-features-for-the-gemini-api-and-google-ai-studio/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1228",
"date": "2024-06-27",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Google",
"title": "Google releases Gemma 2",
"event": "Released Gemma 2 (9B and 27B) under the Gemma license; the 27B runs full-precision inference on a single NVIDIA H100 or A100 80GB GPU or a TPU host",
"significance": "The second Gemma generation; Google claimed the 27B competes with models more than twice its size and the 9B outperforms Llama 3 8B",
"source_url": "https://blog.google/technology/developers/google-gemma-2/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E0400",
"date": "2024-07",
"date_precision": "month",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "Chemnitz University of Technology, Voxar Labs (UFPE)",
"title": "Study on GAN augmentation for wafer dicing defects",
"event": "Study on GAN augmentation for wafer dicing defects — 6 defect classes; the test set uses real images only, excluding synthetic ones. By the authors' measurement, balanced accuracy rose from 65.1% to 88.2% with DCGAN augmentation",
"significance": "Dicing is the first step of back-end processing. A 23-percentage-point gain from synthetic augmentation alone on a small set of defect images speaks directly to the early data-scarce phase of new defect classes",
"source_url": "https://arxiv.org/abs/2407.20268",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0401",
"date": "2024-07",
"date_precision": "month",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
"also_axes": "",
"actor": "EleutherAI",
"title": "EleutherAI releases lm-evaluation-harness v0.4.3",
"event": "lm-evaluation-harness v0.4.3 released — a framework integrating more than 60 academic benchmarks, and the backend of the Open LLM Leaderboard",
"significance": "Created a structure in which evaluation infrastructure is concentrated in a few open-source implementations",
"source_url": "https://github.com/EleutherAI/lm-evaluation-harness",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0402",
"date": "2024-07",
"date_precision": "month",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Liang Wenfeng (梁文锋)",
"title": "Liang Wenfeng sets out DeepSeek's course in a 暗涌 Waves interview",
"event": "Set out DeepSeek's course in an interview with 暗涌 Waves — that it will not go closed source, that Chinese AI cannot remain a follower forever, and that the real gap lies between originality and imitation",
"significance": "Formalized a Chinese lab's open-weight strategy in terms of ecosystem and originality rather than cost savings. Because the original is in Chinese, only the gist is recorded, without quotation marks",
"source_url": "https://thechinaacademy.org/interview-with-deepseek-founder-were-done-following-its-time-to-lead/",
"source_type": "secondary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0403",
"date": "2024-07-02",
"date_precision": "day",
"axis": "21",
"axis_name": "Graph Engineering",
"also_axes": "2",
"actor": "Microsoft Research",
"title": "GraphRAG open-sourced on GitHub with an Azure solution accelerator",
"event": "Open-sourced GraphRAG on GitHub and provided a solution accelerator for deployment on Azure",
"significance": "The point at which a research technique spread as a widely replicable implementation",
"source_url": "https://www.microsoft.com/en-us/research/blog/graphrag-new-tool-for-complex-data-discovery-now-on-github/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0404",
"date": "2024-07-11",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "",
"actor": "Jay Shah et al. (Colfax, Together AI, Meta, NVIDIA, Princeton)",
"title": "FlashAttention-3 published",
"event": "FlashAttention-3 published — Hopper-specific asynchrony delivers 1.5–2.0× in FP16 on H100 (up to 740 TFLOPs/s), and about 1.2 PFLOPs/s in FP8",
"significance": "A case showing that kernels must be rewritten for a specific generation of silicon. Quantitatively rebutted accuracy concerns about FP8 attention",
"source_url": "https://arxiv.org/abs/2407.08608",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0405",
"date": "2024-07-12",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "Official Journal of the EU",
"title": "Regulation (EU) 2024/1689 published in the Official Journal",
"event": "Regulation (EU) 2024/1689 published in the Official Journal — the date of publication",
"significance": "The starting point for counting the entry-into-force date",
"source_url": "https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=OJ:L_202401689",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0406",
"date": "2024-07-16",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Andrej Karpathy",
"title": "Andrej Karpathy announces the founding of Eureka Labs",
"event": "Announced the founding of Eureka Labs — billed as an AI-native school, with teachers and AI teaching assistants teaching together. The first product is LLM101n",
"significance": "A case of a frontier-lab researcher declaring education an independent business",
"source_url": "https://eurekalabs.ai/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1229",
"date": "2024-07-18",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "13",
"actor": "OpenAI",
"title": "OpenAI releases GPT-4o mini",
"event": "GPT-4o mini released — 82% on MMLU (company-reported), $0.15 input and $0.60 output per 1M tokens, 128K context",
"significance": "OpenAI said it is more than 60% cheaper than GPT-3.5 Turbo; it replaced GPT-3.5 for ChatGPT Free, Plus and Team users",
"source_url": "https://openai.com/index/gpt-4o-mini-advancing-cost-efficient-intelligence/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E0407",
"date": "2024-07-23",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "Zhuowan Li et al. (Google DeepMind, University of Michigan)",
"title": "Retrieval Augmented Generation or Long-Context LLMs? published",
"event": "Retrieval Augmented Generation or Long-Context LLMs? published — compares the two approaches and proposes a hybrid that routes based on the model's self-assessment",
"significance": "Framed the long-context vs RAG debate as a cost-performance trade-off",
"source_url": "https://arxiv.org/abs/2407.16833",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0408",
"date": "2024-07-23",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Meta",
"title": "Llama 3.1 (8B/70B/405B)",
"event": "Llama 3.1 (8B/70B/405B)",
"significance": "Frontier-class weights were released for the first time, and the license allowed training on the model's outputs",
"source_url": "https://en.wikipedia.org/wiki/Llama_(language_model)",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0409",
"date": "2024-07-24",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "",
"actor": "Shumailov et al. (Nature)",
"title": "AI models collapse when trained on recursively generated data",
"event": "AI models collapse when trained on recursively generated data",
"significance": "The peak of the model-collapse discourse",
"source_url": "https://www.nature.com/articles/s41586-024-07566-y",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1230",
"date": "2024-07-24",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Mistral AI",
"title": "Mistral AI releases Mistral Large 2",
"event": "Released Mistral Large 2 — 123B, 128K context, dozens of languages and 80+ coding languages; instruct weights published under the Mistral Research License",
"significance": "Published the weights of a 123B flagship designed for single-node inference, limited to research and non-commercial use, with a commercial license required for self-deployment",
"source_url": "https://mistral.ai/news/mistral-large-2407",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E0410",
"date": "2024-07-25",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "Google DeepMind",
"title": "Google DeepMind announces AlphaProof and AlphaGeometry 2 results at IMO 2024",
"event": "AlphaProof and AlphaGeometry 2 IMO 2024 results announced — solved 4 of 6 problems, scoring 28 out of 42 points (top of the silver-medal range). AlphaProof trains itself by proving or disproving candidate statements in Lean",
"significance": "Self-training grounded in a formal language reached olympiad level. DeepMind stated explicitly that it would not have been possible without Lean and Mathlib",
"source_url": "https://deepmind.google/discover/blog/ai-solves-imo-problems-at-silver-medal-level/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0411",
"date": "2024-07-25",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Andrej Karpathy",
"title": "Andrej Karpathy posts the term jagged intelligence on X",
"event": "Posted the term jagged intelligence on X — proposed it as a name for the counterintuitive fact that state-of-the-art LLMs can solve complex math problems yet fail at very simple ones",
"significance": "Gave a name to the gap between benchmark scores and reliability in real use. Salesforce adopted it as a formal research agenda and built the SIMPLE benchmark, and Sundar Pichai riffed on it as AJI",
"source_url": "https://www.marktechpost.com/2024/08/11/andrej-karpathy-coined-a-new-term-jagged-intelligence-understanding-the-inconsistencies-in-advanced-ai/",
"source_type": "secondary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0412",
"date": "2024-07-31",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "",
"actor": "Meta (Llama 3 team)",
"title": "The Llama 3 Herd of Models published",
"event": "The Llama 3 Herd of Models published — over 54 days of pretraining on 16K GPUs there were 466 job interruptions in total, 419 of them unexpected, with about 78% attributable to hardware. Even so, effective training time exceeded 90%",
"significance": "The first primary source to publish quantitative data on fault tolerance in large-scale training. Settled, for the whole industry, that checkpointing and automatic recovery are essential",
"source_url": "https://arxiv.org/abs/2407.21783",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0413",
"date": "2024-08-01",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "EU",
"title": "AI Act enters into force",
"event": "AI Act entry into force — 20 days after publication, Article 113",
"significance": "Entry into force is not application. Application is phased",
"source_url": "https://artificialintelligenceact.eu/article/113/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0414",
"date": "2024-08-02",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "DeepSeek",
"title": "Context Caching on Disk",
"event": "Context Caching on Disk — 90% price cut for cache-hit tokens",
"significance": "Productized the prefix cache; an internal serving optimization surfaced in the API price list",
"source_url": "https://api-docs.deepseek.com/news/news0802/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0415",
"date": "2024-08-06",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "Snell et al.",
"title": "Scaling LLM Test-Time Compute Optimally",
"event": "Scaling LLM Test-Time Compute Optimally",
"significance": "Demonstrated that test-time compute can be more efficient than scaling up parameters",
"source_url": "https://arxiv.org/abs/2408.03314",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0416",
"date": "2024-08-07",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "LG AI Research",
"title": "LG AI Research releases EXAONE 3.0",
"event": "EXAONE 3.0 released — a lightweight model open-sourced for research use; bilingual in Korean and English",
"significance": "The first open release of an in-house LLM by a major Korean company. But its research-only license also revealed the conservatism of the Korean approach to openness",
"source_url": "https://www.lg.co.kr/media/release/27972",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0417",
"date": "2024-08-12",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "US District Court for the Northern District of California (Judge Orrick)",
"title": "Andersen v. Stability AI: motion to dismiss granted in part, denied in part",
"event": "Andersen v. Stability AI motion to dismiss granted in part and denied in part — a ruling on a motion to dismiss, not a judgment on the merits. Direct and induced infringement claims survived; DMCA Section 1202 claims were dismissed in full",
"significance": "The first case over image-generation model training to move into substantive proceedings",
"source_url": "https://law.justia.com/cases/federal/district-courts/california/candce/3:2023cv00201/407208/223/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0418",
"date": "2024-08-13",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "xAI",
"title": "xAI releases Grok-2 / Grok-2 mini in beta",
"event": "Grok-2 / Grok-2 mini released in beta — 56.0% on GPQA and 87.5% on MMLU (company-reported by xAI). Entered anonymously on LMSYS beforehand as sus-column-r",
"significance": "Image generation started not with an in-house model but through integration with an external model (Black Forest Labs FLUX.1)",
"source_url": "https://x.ai/news/grok-2",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0419",
"date": "2024-08-13",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "Sakana AI",
"title": "Sakana AI announces The AI Scientist, with Oxford and UBC",
"event": "The AI Scientist announced (jointly with the University of Oxford and the University of British Columbia) — taking an idea to a paper cost about $15 per paper, and it autonomously carried out the full life cycle of machine learning research with no human involvement beyond the initial setup",
"significance": "Turned the research-automation discourse into a concrete figure: $15 per paper",
"source_url": "https://sakana.ai/ai-scientist-jp/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0420",
"date": "2024-08-13",
"date_precision": "day",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
"also_axes": "",
"actor": "OpenAI",
"title": "OpenAI releases SWE-bench Verified",
"event": "SWE-bench Verified released — 93 developers triple-annotated 1,699 samples to build a 500-sample subset, excluding 68.3% of the original",
"significance": "The reference dataset for evaluating agentic coding. It revealed that a large share of the original benchmark was unfit",
"source_url": "https://openai.com/index/introducing-swe-bench-verified/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0421",
"date": "2024-08-14",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "Anthropic",
"title": "Anthropic launches prompt caching in public beta",
"event": "Prompt caching released in public beta — cache reads 90% cheaper, cache writes +25%",
"significance": "Cache economics became visible on the price list and went on to become a key variable in agent economics",
"source_url": "https://claude.com/blog/prompt-caching",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0422",
"date": "2024-08-15",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Nous Research",
"title": "Hermes 3",
"event": "Hermes 3 — a full-parameter fine-tune based on Llama 3.1 405B",
"significance": "The first case of the community actually fine-tuning the 405B model",
"source_url": "https://huggingface.co/NousResearch/Hermes-3-Llama-3.1-405B",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0423",
"date": "2024-08-15",
"date_precision": "day",
"axis": "21",
"axis_name": "Graph Engineering",
"also_axes": "2",
"actor": "Peng, Zhu et al. (Peking University, Ant Group, and others)",
"title": "Graph Retrieval-Augmented Generation",
"event": "Graph Retrieval-Augmented Generation: A Survey published — the first comprehensive GraphRAG survey, organizing the graph-based indexing, retrieval and generation workflow",
"significance": "Brought a sprawl of variants under one taxonomy, formalizing graph-based RAG as a research field of its own",
"source_url": "https://arxiv.org/abs/2408.08921",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0424",
"date": "2024-08-20",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "",
"actor": "Epoch AI",
"title": "Can AI scaling continue through 2030?",
"event": "Can AI scaling continue through 2030?",
"significance": "Estimated 400 trillion to 20 quadrillion effective tokens by 2030, and laid out the order of constraints: power binds first, then chip production capacity",
"source_url": "https://epoch.ai/blog/can-ai-scaling-continue-through-2030",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0425",
"date": "2024-08-26",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "",
"actor": "Nous Research",
"title": "Nous Research publishes DisTrO preliminary report",
"event": "DisTrO preliminary report published — a family of optimizers that cuts inter-GPU communication by 3–4 orders of magnitude",
"significance": "The starting point of the approach of training over internet bandwidth, without data center interconnects",
"source_url": "https://github.com/NousResearch/DisTrO",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0426",
"date": "2024-08-27",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "",
"actor": "Cerebras",
"title": "Cerebras launches Cerebras Inference",
"event": "Cerebras Inference launched",
"significance": "Used wafer-scale SRAM to get around the bandwidth limits of GPU HBM, popularizing the category of inference-only chips",
"source_url": "https://www.cerebras.ai/press-release/cerebras-launches-the-worlds-fastest-ai-inference",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0427",
"date": "2024-09",
"date_precision": "month",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "",
"actor": "xAI",
"title": "xAI brings Colossus online in Memphis",
"event": "Colossus in Memphis came online — 100,000 H100 GPUs installed in 122 days, then expanded to 200,000 in 92 days",
"significance": "The fastest build-out in the industry, by the company's own announcement. But the initial grid connection was only 8 MW, so it relied on temporary gas turbines, and was stepped up to 150 MW only in November",
"source_url": "https://x.ai/colossus",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0428",
"date": "2024-09-07",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "Michael Günther et al. (Jina AI)",
"title": "Jina AI presents Late Chunking",
"event": "Late Chunking presented — the whole document is first passed through a long-context embedding model, then the token embeddings are pooled chunk by chunk",
"significance": "Solved the problem of chunking cutting off context by changing the order of embedding",
"source_url": "https://arxiv.org/abs/2409.04701",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0429",
"date": "2024-09-10",
"date_precision": "day",
"axis": "21",
"axis_name": "Graph Engineering",
"also_axes": "2",
"actor": "Ant Group (Liang, Sun et al.)",
"title": "Ant Group releases KAG (Knowledge Augmented Generation)",
"event": "KAG (Knowledge Augmented Generation) released — a framework that combines knowledge graphs and vector retrieval to raise LLM performance in professional domains",
"significance": "A case of Chinese Big Tech industrializing the GraphRAG lineage for professional domains (finance, healthcare)",
"source_url": "https://arxiv.org/abs/2409.13731",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1231",
"date": "2024-09-11",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Upstage",
"title": "Upstage releases Solar Pro Preview",
"event": "Released Solar Pro Preview — 22B, designed to run on a single GPU, English-only with a 4K context window, open source with commercial use allowed (MIT)",
"significance": "A preview released as weights plus free API access ahead of the full release planned for November; Upstage claimed it competes with multi-GPU models such as Llama 3.1 70B",
"source_url": "https://www.upstage.ai/news/solar-pro-preview",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E0430",
"date": "2024-09-12",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "OpenAI",
"title": "o1-preview / o1-mini",
"event": "o1-preview / o1-mini — Learning to Reason with LLMs",
"significance": "The first commercial reasoning model; on AIME 2024, from 12% for GPT-4o to 74% for o1",
"source_url": "https://openai.com/index/learning-to-reason-with-llms/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0431",
"date": "2024-09-16",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Ha Jung-woo (head of Naver Future AI Center)",
"title": "Ha Jung-woo defines sovereign AI and says it should be a government agenda",
"event": "Defined sovereign AI as AI that better understands a country's culture, and said it should become a government agenda rather than a Naver agenda. Proposed giving 5,000 GPUs to 3–4 national-champion AI companies and having them release the results as open source",
"significance": "The turning point at which sovereign AI in Korea moved from a corporate marketing term to a national policy term. This person became head of the government's AI policy 9 months later",
"source_url": "https://zdnet.co.kr/view/?no=20240916121827",
"source_type": "secondary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0432",
"date": "2024-09-17",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "",
"actor": "Alexandre Défossez et al. (Kyutai)",
"title": "Kyutai unveils Moshi",
"event": "Moshi presented — a full-duplex speech-to-speech model that does away with the VAD, ASR and TTS pipeline. Theoretical latency 160 ms, measured 200 ms. Inner Monologue: time-aligned text tokens are generated before the audio tokens",
"significance": "The lineage that replaced pipeline-based voice dialogue with a single model",
"source_url": "https://arxiv.org/abs/2410.00037",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0433",
"date": "2024-09-18",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Zayne Sprague et al. (University of Texas at Austin and others)",
"title": "Zayne Sprague et al. publish \"To CoT or not to CoT?\"",
"event": "\"To CoT or not to CoT?\" published — a meta-analysis of over 100 papers plus experiments reported that CoT gains are concentrated in math and symbolic reasoning",
"significance": "A rebuttal that empirically narrowed the scope in which CoT applies",
"source_url": "https://arxiv.org/abs/2409.12183",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0434",
"date": "2024-09-18",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "",
"actor": "Voyage AI",
"title": "Voyage AI releases voyage-3",
"event": "voyage-3 released — 7.55% better on average than OpenAI text-embedding-3-large, at 2.2× lower cost and with 3× smaller dimensions (company-reported)",
"significance": "The phase in which competition among commercial embedding APIs explicitly took aim at the OpenAI baseline",
"source_url": "https://blog.voyageai.com/2024/09/18/voyage-3/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0435",
"date": "2024-09-19",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "Anthropic",
"title": "Contextual Retrieval",
"event": "Contextual Retrieval",
"significance": "Prepending 50–100 tokens of LLM-generated context to each chunk cut the retrieval failure rate by 35%; by 49% when combined with BM25, and by 67% with reranking added",
"source_url": "https://www.anthropic.com/news/contextual-retrieval",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1232",
"date": "2024-09-19",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Alibaba Qwen",
"title": "Alibaba releases Qwen2.5",
"event": "Released Qwen2.5 — seven sizes from 0.5B to 72B, pretrained on up to 18T tokens, 128K context, 29+ languages; Qwen2.5-Coder and Qwen2.5-Math released alongside",
"significance": "All open models except the 3B and 72B were released under Apache 2.0, and the 14B and 32B sizes missing from the previous generation returned",
"source_url": "https://qwenlm.github.io/blog/qwen2.5/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E0436",
"date": "2024-09-23",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Sam Altman",
"title": "Sam Altman publishes \"The Intelligence Age\"",
"event": "\"The Intelligence Age\" posted — diagnosed that deep learning worked and got predictably better with scale, and said superintelligence might arrive \"in a few thousand days\"",
"significance": "The phrase \"a few thousand days\" became the single most-quoted line in the 2024–25 timeline debate. Its vague unit itself became a point of contention",
"source_url": "https://ia.samaltman.com/",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E1233",
"date": "2024-09-25",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "7;19",
"actor": "Meta",
"title": "Meta releases Llama 3.2 with vision and on-device models",
"event": "Released Llama 3.2 — 11B and 90B vision LLMs plus lightweight 1B and 3B text models (128K context) for edge and mobile devices, under the Llama 3.2 Community License",
"significance": "The 11B and 90B were the first Llama models to support image input, released alongside 1B and 3B models aimed at on-device use",
"source_url": "https://ai.meta.com/blog/llama-3-2-connect-2024-vision-edge-mobile-devices/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E0437",
"date": "2024-09-29",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "Governor of California",
"title": "California governor vetoes SB 1047",
"event": "SB 1047 vetoed — the date of the veto. Cited the lack of risk-based analysis, saying compute-cost thresholds create a false sense of security",
"significance": "The first setback for legislation regulating frontier models",
"source_url": "https://www.gov.ca.gov/wp-content/uploads/2024/09/SB-1047-Veto-Message.pdf",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0438",
"date": "2024-10",
"date_precision": "month",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Dario Amodei",
"title": "Dario Amodei publishes \"Machines of Loving Grace\"",
"event": "\"Machines of Loving Grace\" posted — said he dislikes the term AGI and offered \"powerful AI\" instead, arguing that AI-enabled biology could compress the progress of the next 50–100 years into 5–10 years",
"significance": "Established \"powerful AI\" in place of AGI, and \"a country of geniuses in a datacenter\", as common vocabulary. The first serious optimistic document to come out of the safety-discourse camp",
"source_url": "https://darioamodei.com/machines-of-loving-grace",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0439",
"date": "2024-10-01",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "OpenAI",
"title": "API Prompt Caching",
"event": "API Prompt Caching — 50% discount on cached input tokens",
"significance": "The last of the three companies to productize prefix caching",
"source_url": "https://openai.com/index/api-prompt-caching/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0440",
"date": "2024-10-01",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "",
"actor": "OpenAI",
"title": "OpenAI releases the Realtime API",
"event": "Realtime API released",
"significance": "Voice becomes a developer primitive (speech-to-speech over WebSocket)",
"source_url": "https://openai.com/index/introducing-the-realtime-api/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0441",
"date": "2024-10-01",
"date_precision": "day",
"axis": "21",
"axis_name": "Graph Engineering",
"also_axes": "2",
"actor": "gusye1234 (open-source community)",
"title": "nano-graphrag v0.0.8 released: GraphRAG's core in about 1,100 lines",
"event": "Release of nano-graphrag v0.0.8, which reimplemented GraphRAG's core features in about 1,100 lines of code",
"significance": "Community complaints that the official implementation was heavyweight surfaced as a hackable, ultra-light reimplementation",
"source_url": "https://github.com/gusye1234/nano-graphrag",
"source_type": "primary",
"claim_type": "community",
"added_week": "2026-W35"
},
{
"id": "E0442",
"date": "2024-10-02",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "BIS",
"title": "BIS creates DC VEU (Data Center Validated End User)",
"event": "DC VEU (Data Center Validated End User) created",
"significance": "Gave data center operators in third countries a pre-approval pathway",
"source_url": "https://www.federalregister.gov/documents/2024/10/02/2024-22587/expansion-of-validated-end-user-authorization-data-center-validated-end-user-authorization",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0443",
"date": "2024-10-07",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Iman Mirzadeh et al. (Apple)",
"title": "Apple researchers present GSM-Symbolic",
"event": "GSM-Symbolic presented — performance drops when templates swap the numbers and names, or when an irrelevant clause is added",
"significance": "Evidence that math reasoning may not be formal reasoning",
"source_url": "https://arxiv.org/abs/2410.05229",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0444",
"date": "2024-10-08",
"date_precision": "day",
"axis": "21",
"axis_name": "Graph Engineering",
"also_axes": "2",
"actor": "HKU (Guo, Xia, Huang et al.)",
"title": "HKU releases LightRAG",
"event": "LightRAG released — a dual-level retrieval and incremental-update framework combining graph structures with vector representations",
"significance": "The open-source camp's leading lightweight alternative to the costly indexing of Microsoft GraphRAG",
"source_url": "https://arxiv.org/abs/2410.05779",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0445",
"date": "2024-10-09",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "",
"actor": "Wanchao Liang et al. (Meta)",
"title": "Meta presents TorchTitan",
"event": "TorchTitan presented — modular 3D parallelism and elastic scaling. An additional 30% speedup with 3D parallelism for 405B on 512 GPUs",
"significance": "The point at which a PyTorch-native training framework became established as an alternative to Megatron-LM. FSDP2, torch.compile and FP8 were bundled into a single stack",
"source_url": "https://arxiv.org/abs/2410.06511",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0446",
"date": "2024-10-10",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "National Institute of Advanced Industrial Science and Technology (AIST), Japan",
"title": "AIST announces ABCI 3.0",
"event": "ABCI 3.0 announced — 6,128 NVIDIA H200 GPUs in total, 6.2 exaflops peak at half precision (about 7 times the previous system), general availability from mid-January 2025",
"significance": "The pillar that moved Japan's national AI compute up a generation. A national compute model shared by private startups and universities",
"source_url": "https://www.aist.go.jp/aist_j/news/pr20241010.html",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0447",
"date": "2024-10-10",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "NEDO, METI (Japan)",
"title": "GENIAC selects 20 AI foundation model projects and 3 data-use demonstrations",
"event": "GENIAC selected 20 AI foundation model development projects and 3 data-utilization demonstration projects — in the form of subsidies for compute usage fees",
"significance": "An approach in which the state directly subsidizes GPU usage fees to grow many domestic foundation models in parallel. An industrial policy that spreads its bets rather than picking winners",
"source_url": "https://www.nedo.go.jp/news/press/AA5_101790.html",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0448",
"date": "2024-10-12",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "Xi Jiang, Jian Li, Chengjie Wang et al.",
"title": "Xi Jiang et al. release the MMAD benchmark",
"event": "MMAD benchmark released — 8,366 industrial images and 39,672 questions. The best performer measured by the authors, GPT-4o, averaged 74.9% accuracy, and the paper states that this falls far short of industrial requirements",
"significance": "The upper bound for the idea of handing defect judgment directly to general-purpose multimodal LLMs. 74.9% is not a level usable for final-inspection decisions",
"source_url": "https://arxiv.org/abs/2410.09453",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0449",
"date": "2024-10-14",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Anthropic",
"title": "Anthropic launches prompt improver in the Console",
"event": "Prompt improver launched in the Console — automatically applies 5 transformations, including adding a CoT section, standardizing examples in XML, and rewriting for clarity",
"significance": "The point at which prompt-engineering techniques were absorbed into a product feature",
"source_url": "https://claude.com/blog/prompt-improver",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0450",
"date": "2024-10-15",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Preferred Networks / Preferred Elements",
"title": "GENIAC cycle 1 yields PLaMo-100B-Pretrained (100B parameters, from scratch)",
"event": "PLaMo-100B-Pretrained released as an output of GENIAC cycle 1 (100 billion parameters, trained from scratch). A dual structure of a non-commercial research license and a separate commercial license",
"significance": "The first confirmed case of Japanese state funding actually producing a domestic 100B-class model",
"source_url": "https://www.preferred.jp/ja/news/pr20241015",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0451",
"date": "2024-10-15",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "Anthropic",
"title": "RSP v2.0",
"event": "RSP v2.0",
"significance": "Overhauled the capability-threshold framework",
"source_url": "https://www.anthropic.com/rsp-updates",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0452",
"date": "2024-10-22",
"date_precision": "day",
"axis": "4",
"axis_name": "MCP & Skills Standardization",
"also_axes": "",
"actor": "Anthropic",
"title": "Anthropic releases Claude 3.5 Sonnet computer use in public beta",
"event": "Claude 3.5 Sonnet computer use in public beta",
"significance": "The first frontier model to operate GUIs through screenshots and coordinates, without dedicated APIs",
"source_url": "https://www.anthropic.com/news/3-5-models-and-computer-use",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0453",
"date": "2024-10-23",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "Google DeepMind",
"title": "Google DeepMind publishes SynthID-Text in Nature",
"event": "SynthID-Text paper published in Nature — tournament-sampling watermarking applied to Gemini in production. In a comparison of about 20 million real responses, the drop in quality was not statistically significant",
"significance": "The first case of provenance marking for generated content moving beyond research into large-scale deployment",
"source_url": "https://www.nature.com/articles/s41586-024-08025-4",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0454",
"date": "2024-10-23",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Noam Brown",
"title": "Noam Brown presents the test-time compute thesis at TED AI",
"event": "Presented the test-time compute thesis at TED AI — cited the poker case in which letting a bot think for just 20 seconds gave the same performance gain as scaling the model up 100,000 times and training it 100,000 times longer",
"significance": "Shifted the axis from pretraining scaling to inference-time scaling. Later became the line cited as grounds in the reasoning-model discourse",
"source_url": "https://venturebeat.com/ai/openai-noam-brown-stuns-ted-ai-conference-20-seconds-of-thinking-worth-100000x-more-data",
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"actor": "OSI",
"title": "Open Source AI Definition 1.0",
"event": "Open Source AI Definition 1.0 — 3 components: Data Information, Code and Parameters",
"significance": "Established the grounds for ruling that most of Llama, Gemma and Qwen are not open source",
"source_url": "https://opensource.org/ai/open-source-ai-definition",
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{
"id": "E0456",
"date": "2024-10-28",
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"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "Anthropic (Benton et al.)",
"title": "Sabotage Evaluations for Frontier Models",
"event": "Sabotage Evaluations for Frontier Models",
"significance": "Formalized a model's capacity to undermine human oversight, evaluation and decision-making as a distinct risk category",
"source_url": "https://arxiv.org/abs/2410.21514",
"source_type": "primary",
"claim_type": "fact",
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},
{
"id": "E0457",
"date": "2024-10-31",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "Physical Intelligence",
"title": "π0 — flow matching, up to 50 Hz, 8 robot types",
"event": "π0 — flow matching, up to 50 Hz, 8 robot types",
"significance": "The design archetype for commercial VLAs",
"source_url": "https://www.pi.website/blog/pi0",
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"claim_type": "fact",
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{
"id": "E0458",
"date": "2024-11-04",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "",
"actor": "xAI",
"title": "xAI opens the Grok API in public beta",
"event": "Grok API in public beta — 128,000-token context, compatible with the OpenAI and Anthropic REST APIs",
"significance": "A strategy of lowering switching costs by adopting compatibility with competitors' APIs",
"source_url": "https://x.ai/news/api",
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"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0459",
"date": "2024-11-07",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "",
"actor": "Adam Fourney et al. (Microsoft Research)",
"title": "Microsoft Research presents Magentic-One",
"event": "Magentic-One presented — a general-purpose multi-agent system in which an Orchestrator directs WebSurfer, FileSurfer, Coder and ComputerTerminal",
"significance": "A reference implementation of the orchestrator-worker pattern",
"source_url": "https://arxiv.org/abs/2411.04468",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0460",
"date": "2024-11-07",
"date_precision": "day",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
"also_axes": "",
"actor": "Epoch AI (Elliot Glazer et al.)",
"title": "Epoch AI releases FrontierMath",
"event": "FrontierMath released — hundreds of unpublished math problems that take researchers hours to days to solve. At release, the SOTA solve rate was under 2%",
"significance": "A holdout design that cannot be contaminated",
"source_url": "https://arxiv.org/abs/2411.04872",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
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{
"id": "E0461",
"date": "2024-11-07",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "",
"actor": "Tanishq Kumar et al. (Harvard)",
"title": "Tanishq Kumar et al. publish \"Scaling Laws for Precision\"",
"event": "\"Scaling Laws for Precision\" published — proposed precision-aware scaling laws in which low-precision training reduces a model's effective parameter count. The more training data, the greater the degradation from post-training quantization",
"significance": "A study that tested, at the level of scaling laws, whether FP8/FP4 works. Redefined precision not as a free optimization but as a trade against capacity",
"source_url": "https://arxiv.org/abs/2411.04330",
"source_type": "primary",
"claim_type": "fact",
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},
{
"id": "E0462",
"date": "2024-11-09",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "The Information",
"title": "The Information reports slowing gains for OpenAI's Orion",
"event": "Report that OpenAI Orion's performance gains were slowing",
"significance": "Ignited the scaling-wall debate",
"source_url": "https://techcrunch.com/2024/11/09/openai-reportedly-developing-new-strategies-to-deal-with-ai-improvement-slowdown",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0463",
"date": "2024-11-09",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Gary Marcus",
"title": "CONFIRMED",
"event": "\"CONFIRMED: LLMs have indeed reached a point of diminishing returns\" posted — claimed that his own 2022 prediction had been borne out",
"significance": "The turning point at which the scaling-limits debate moved from the critics' camp to reporting from inside the industry",
"source_url": "https://garymarcus.substack.com/p/confirmed-llms-have-indeed-reached",
"source_type": "secondary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0464",
"date": "2024-11-11",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "Ilya Sutskever (Reuters)",
"title": "Ilya Sutskever says results from scaling up pretraining have plateaued",
"event": "Said that results from scaling up pretraining have plateaued",
"significance": "The first public statement of skepticism about pretraining scaling",
"source_url": "https://www.malaymail.com/news/money/2024/11/12/openai-and-rivals-seek-new-path-to-smarter-ai-as-current-methods-hit-limitations/156551",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
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{
"id": "E0465",
"date": "2024-11-21",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "Ai2",
"title": "Ai2 releases Tülu 3 fully open and formalizes RLVR",
"event": "Tülu 3 released fully open, and RLVR formalized",
"significance": "Named the approach of replacing the reward model with a verifier function, and released every stage of the pipeline",
"source_url": "https://allenai.org/blog/tulu-3-technical",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0466",
"date": "2024-11-22",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "",
"actor": "AI2",
"title": "Tülu 3",
"event": "Tülu 3 — the full recipe released, running from SFT to DPO to RLVR",
"significance": "The turning point from preference learning to verifiable rewards",
"source_url": "https://arxiv.org/abs/2411.15124",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0467",
"date": "2024-11-25",
"date_precision": "day",
"axis": "4",
"axis_name": "MCP & Skills Standardization",
"also_axes": "",
"actor": "Anthropic",
"title": "Anthropic announces MCP (spec version 2024-11-05)",
"event": "MCP announced (spec version 2024-11-05)",
"significance": "Replaced the combinatorial explosion of N models times M data sources with a single open standard",
"source_url": "https://www.anthropic.com/news/model-context-protocol",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0468",
"date": "2024-11-25",
"date_precision": "day",
"axis": "21",
"axis_name": "Graph Engineering",
"also_axes": "2",
"actor": "Microsoft Research (Edge, Trinh, Larson)",
"title": "Microsoft Research releases LazyGraphRAG",
"event": "LazyGraphRAG released — combines best-first and breadth-first search without upfront summarization; claimed its indexing cost is 0.1% of full GraphRAG and that it cuts global query cost by more than 700 times",
"significance": "The original GraphRAG authors acknowledged the cost problem themselves and turned toward deferred graph construction",
"source_url": "https://www.microsoft.com/en-us/research/blog/lazygraphrag-setting-a-new-standard-for-quality-and-cost/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0469",
"date": "2024-11-28",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "Alibaba Qwen",
"title": "Alibaba Qwen releases QwQ-32B-Preview",
"event": "QwQ-32B-Preview released",
"significance": "The first open reasoning model after o1",
"source_url": "https://qwenlm.github.io/blog/qwq-32b-preview/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0470",
"date": "2024-12",
"date_precision": "month",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "DeepSeek",
"title": "DeepSeek-V3 (671B / 37B active, MIT), about 2,788K GPU hours",
"event": "DeepSeek-V3 (671B / 37B active, MIT), about 2,788K GPU hours",
"significance": "With an MIT license and extreme cost efficiency, the Chinese camp took the most open position",
"source_url": "https://en.wikipedia.org/wiki/DeepSeek",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0471",
"date": "2024-12-02",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "",
"actor": "Nous Research",
"title": "Nous Research releases DeMo paper and code; 15B model trained with DisTrO",
"event": "DeMo paper and code released; training of a 15B model with DisTrO completed",
"significance": "Claims at the level of a preliminary report turned into a reproducible paper and code",
"source_url": "https://github.com/NousResearch/DisTrO",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0472",
"date": "2024-12-05",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "OpenAI",
"title": "OpenAI launches ChatGPT Pro at $200 a month, full o1 and o1 pro mode",
"event": "ChatGPT Pro at $200 a month, plus the full release of o1 and o1 pro mode",
"significance": "The commercialization of inference-scaling: users pay for the model to think longer",
"source_url": "https://openai.com/index/introducing-chatgpt-pro/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0473",
"date": "2024-12-05",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "BIS",
"title": "BIS expands the FDPR, adds HBM controls and 140 Entity List entries",
"event": "FDPR expanded and new HBM controls introduced; 140 entities added to the Entity List",
"significance": "Memory became an axis of control for the first time",
"source_url": "https://www.federalregister.gov/documents/2024/12/05/2024-28270/foreign-produced-direct-product-rule-additions-and-refinements-to-controls-for-advanced-computing",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1234",
"date": "2024-12-06",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Meta",
"title": "Meta releases Llama 3.3 70B",
"event": "Released Llama 3.3 70B — a text-only 70B model with 128K context, pretrained on about 15T tokens, supporting 8 languages, under the Llama 3.3 Community License",
"significance": "An update to the Llama 3 line that shipped a single 70B instruction-tuned model, with no separate pretrained release",
"source_url": "https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E0474",
"date": "2024-12-08",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "",
"actor": "Keller Jordan",
"title": "Keller Jordan releases the Muon optimizer",
"event": "Muon optimizer released",
"significance": "Set new training-speed records over AdamW using Newton-Schulz orthogonalization",
"source_url": "https://kellerjordan.github.io/posts/muon/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1235",
"date": "2024-12-09",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "LG AI Research",
"title": "LG AI Research open-sources EXAONE 3.5",
"event": "Released EXAONE 3.5 as open source — three sizes, 2.4B (on-device), 7.8B and 32B, with 32K context, under the EXAONE AI Model License Agreement 1.1 - NC",
"significance": "A follow-up four months after the EXAONE 3.0 7.8B release that expanded to three sizes while keeping a non-commercial license",
"source_url": "https://www.lgresearch.ai/blog/view?seq=506",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E1236",
"date": "2024-12-11",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "3;7",
"actor": "Google DeepMind",
"title": "Google introduces Gemini 2.0",
"event": "Gemini 2.0 introduced — first model, an experimental Gemini 2.0 Flash, with native image and audio output and native tool use",
"significance": "Google claimed 2.0 Flash outperforms 1.5 Pro on key benchmarks at twice the speed, launched the Multimodal Live API and Deep Research, and presented agent prototypes such as Project Astra, Project Mariner and Jules",
"source_url": "https://blog.google/technology/google-deepmind/google-gemini-ai-update-december-2024/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E0475",
"date": "2024-12-13",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "",
"actor": "Meta (Pagnoni et al.)",
"title": "Byte Latent Transformer",
"event": "Byte Latent Transformer",
"significance": "Processes bytes directly, without a fixed vocabulary, using entropy-based dynamic patches; the first large-scale scaling of the tokenizer-free approach",
"source_url": "https://arxiv.org/abs/2412.09871",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0476",
"date": "2024-12-13",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Ilya Sutskever",
"title": "Ilya Sutskever gives the NeurIPS 2024 Test of Time talk",
"event": "NeurIPS 2024 Test of Time talk — declared that pre-training as we know it will unquestionably end, likened data to the fossil fuel of AI, and diagnosed that peak data has been reached",
"significance": "Scaling skepticism came not from a critic but from one of the founders of the scaling paradigm. Peak data became an industry-standard term",
"source_url": "https://www.theverge.com/2024/12/13/24320811/what-ilya-sutskever-sees-openai-model-data-training",
"source_type": "secondary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0477",
"date": "2024-12-18",
"date_precision": "day",
"axis": "5",
"axis_name": "Loop & Harness Engineering",
"also_axes": "",
"actor": "Xu et al. (TheAgentCompany)",
"title": "The phrase \"agent harness\" appears in the body of a paper",
"event": "The phrase \"agent harness\" appears in the body text of a paper",
"significance": "The full text confirms that the term was used in the evaluation community first, before it went mainstream",
"source_url": "https://arxiv.org/html/2412.14161v3",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0478",
"date": "2024-12-18",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "Anthropic, Redwood Research",
"title": "Alignment Faking in LLMs",
"event": "Alignment Faking in LLMs",
"significance": "Observed, even without explicit training for it, the phenomenon of complying only while in training and concealing preferences",
"source_url": "https://www.anthropic.com/research/alignment-faking",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0479",
"date": "2024-12-19",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "",
"actor": "Anthropic",
"title": "Building Effective AI Agents",
"event": "Building Effective AI Agents",
"significance": "Distinguished workflows from agents and recommended starting with direct API calls rather than frameworks; a summing-up of 2 years of hype",
"source_url": "https://www.anthropic.com/engineering/building-effective-agents",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0480",
"date": "2024-12-19",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "Google",
"title": "Gemini 2.0 Flash Thinking Experimental",
"event": "Gemini 2.0 Flash Thinking Experimental",
"significance": "Google's first reasoning model, taking the route of showing its thinking process to users",
"source_url": "https://9to5google.com/2024/12/19/gemini-2-0-flash-thinking/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0481",
"date": "2024-12-20",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "Brian J Chan et al. (National Chengchi University, Academia Sinica)",
"title": "Brian J Chan et al. present Cache-Augmented Generation",
"event": "Cache-Augmented Generation presented — knowledge is placed in the context in advance and the KV cache is preloaded, eliminating the retrieval step",
"significance": "A branch that skips RAG altogether in the long-context era",
"source_url": "https://arxiv.org/abs/2412.15605",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0482",
"date": "2024-12-20",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "OpenAI (Guan, Wallace, Barak et al.)",
"title": "Deliberative Alignment",
"event": "Deliberative Alignment",
"significance": "Trains models to reason explicitly over safety specifications in CoT; the basic recipe for aligning reasoning models",
"source_url": "https://arxiv.org/abs/2412.16339",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0483",
"date": "2024-12-20",
"date_precision": "day",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
"also_axes": "",
"actor": "François Chollet / ARC Prize",
"title": "ARC Prize releases o3's ARC-AGI results",
"event": "o3's ARC-AGI results released — 75.7% in high-efficiency mode ($26 per task), 87.5% in low-efficiency mode ($4,560 per task). At the same time, stated explicitly that o3 is not yet AGI",
"significance": "Created the practice of reporting capability and cost together",
"source_url": "https://arcprize.org/blog/oai-o3-pub-breakthrough",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0484",
"date": "2024-12-23",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "xAI",
"title": "xAI raises a $6 billion Series C",
"event": "$6 billion Series C. NVIDIA and AMD joined as strategic investors",
"significance": "A structure in which chip suppliers came in directly as investors",
"source_url": "https://x.ai/news/series-c",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0485",
"date": "2024-12-26",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "National Assembly of the Republic of Korea",
"title": "National Assembly passes Korea's AI Basic Act in plenary session",
"event": "The Framework Act on the Development of Artificial Intelligence and the Establishment of a Foundation for Trust passed the plenary session — the date of passage",
"significance": "The world's second comprehensive AI law, after the EU's",
"source_url": "https://www.shinkim.com/eng/media/newsletter/2667",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0486",
"date": "2024-12-27",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "",
"actor": "DeepSeek-AI",
"title": "DeepSeek-V3 Technical Report",
"event": "DeepSeek-V3 Technical Report — a 671B/37B MoE trained in FP8",
"significance": "Established low-precision pretraining at frontier scale",
"source_url": "https://arxiv.org/abs/2412.19437",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0487",
"date": "2024-12-27",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Geoffrey Hinton",
"title": "Geoffrey Hinton raises the odds of AI wiping out humanity in 30 years to 10–20%",
"event": "In a BBC interview, raised his estimate of the probability that AI leads to human extinction within the next 30 years to 10–20%",
"significance": "Turned a qualitative warning into a number. Since then, the most widely cited reference value in the p(doom) debate",
"source_url": "https://www.theguardian.com/technology/2024/dec/27/godfather-of-ai-raises-odds-of-the-technology-wiping-out-humanity-over-next-30-years",
"source_type": "secondary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0488",
"date": "2025-01-06",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "Sam Altman",
"title": "Reflections",
"event": "Reflections — expressed confidence in knowing how to build AGI as it has traditionally been understood",
"significance": "The most direct AGI declaration yet by a frontier-lab CEO, moving the goal on to superintelligence",
"source_url": "https://blog.samaltman.com/reflections",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0489",
"date": "2025-01-06",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "NVIDIA",
"title": "NVIDIA releases Cosmos world foundation models",
"event": "Cosmos world foundation models released — trained on 20 million hours of real-world interaction, industrial, robotics and driving video, under a license that permits commercial use",
"significance": "Productized synthetic-data and simulation infrastructure for training physical AI",
"source_url": "https://blogs.nvidia.com/blog/cosmos-world-foundation-models/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0490",
"date": "2025-01-07",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Kai-Fu Lee (李开复, 01.AI/零一万物)",
"title": "Kai-Fu Lee publicly abandons pretraining of ultra-large models",
"event": "Publicly declared an end to pretraining ultra-large-parameter models — said only big companies can afford to burn through giant models, and that a startup's way to survive is not burning more GPUs but making every yuan count. Staff for ultra-large-model training were moved to a joint lab with Alibaba Cloud",
"significance": "The first public declaration of a change of course among China's \"six little tigers\". The point at which startups giving up frontier pretraining became an established narrative",
"source_url": "https://www.nbd.com.cn/articles/2025-01-07/3713092.html",
"source_type": "secondary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0491",
"date": "2025-01-09",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "Xiaoxi Li et al. (Renmin University of China)",
"title": "Xiaoxi Li et al. present Search-o1",
"event": "Search-o1 presented — inserts search into a reasoning model's thinking process and condenses documents with a Reason-in-Documents module",
"significance": "Integrated agentic retrieval into the reasoning model's inner loop",
"source_url": "https://arxiv.org/abs/2501.05366",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0492",
"date": "2025-01-15",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "Aditi Singh et al. (Cleveland State University and others)",
"title": "Aditi Singh et al. publish an Agentic RAG survey",
"event": "Agentic RAG survey published — proposed a RAG taxonomy that includes reflection, planning, tool use and multi-agent setups",
"significance": "A sign that agentic retrieval had settled in as a category of its own",
"source_url": "https://arxiv.org/abs/2501.09136",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0493",
"date": "2025-01-15",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "François Chollet",
"title": "François Chollet leaves Google and founds Ndea, a lab dedicated to AGI",
"event": "Left Google and founded Ndea, a research lab dedicated to AGI",
"significance": "The designer of ARC put capital and an organization behind the alternative route of program synthesis",
"source_url": "https://ndea.com/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0494",
"date": "2025-01-15",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "BIS",
"title": "BIS issues the AI Diffusion Framework final rule",
"event": "AI Diffusion Framework final rule — a 3-tier worldwide compute quota",
"significance": "The most far-reaching regulatory plan, making even allies subject to compute rationing",
"source_url": "https://www.federalregister.gov/documents/2025/01/15/2025-00636/framework-for-artificial-intelligence-diffusion",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0495",
"date": "2025-01-19",
"date_precision": "day",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
"also_axes": "",
"actor": "TechCrunch",
"title": "TechCrunch reports criticism of Epoch AI for not disclosing OpenAI funding",
"event": "Reported criticism of Epoch AI for not disclosing OpenAI funding — contributing mathematicians raised the problem that they had not known about OpenAI's involvement",
"significance": "The conflict of interest in who funds benchmark creation became a public issue",
"source_url": "https://techcrunch.com/2025/01/19/ai-benchmarking-organization-criticized-for-waiting-to-disclose-funding-from-openai/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0496",
"date": "2025-01-20",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "3",
"actor": "Zep (Rasmussen, Chalef et al.)",
"title": "Zep releases agent memory built on Graphiti, a temporal knowledge graph engine",
"event": "Released an agent memory architecture built on Graphiti, a temporally aware knowledge graph engine — claimed an edge over prior approaches, including 94.8% on the DMR benchmark",
"significance": "A leading example of putting temporal knowledge graphs forward as a first-class design element of agent memory",
"source_url": "https://arxiv.org/abs/2501.13956",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0497",
"date": "2025-01-20",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "DeepSeek, Moonshot",
"title": "DeepSeek releases DeepSeek-R1 (MIT); Kimi K1.5 arrives the same day",
"event": "DeepSeek-R1 (MIT) released, with Kimi K1.5 on the same day",
"significance": "Reasoning models were immediately reproduced as open weights, and 2 Chinese labs declared their pursuit of the frontier on the same day",
"source_url": "https://en.wikipedia.org/wiki/Moonshot_AI",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0498",
"date": "2025-01-21",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "OpenAI, SoftBank, Oracle, MGX",
"title": "OpenAI, SoftBank, Oracle and MGX announce Stargate",
"event": "Announced Stargate — $500 billion over 4 years, with $100 billion deployed immediately",
"significance": "The symbolic starting point of the private AI infrastructure capex cycle",
"source_url": "https://openai.com/index/announcing-the-stargate-project/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0499",
"date": "2025-01-21",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "Government of the Republic of Korea",
"title": "South Korea promulgates the AI Basic Act",
"event": "Promulgated the AI Basic Act — Act No. 20676; this is the promulgation date. Under the addenda it takes effect 1 year after promulgation",
"significance": "This is the promulgation date, not the effective date. The addenda set a 1-year grace period, so it must be kept distinct from the Jan 22, 2026 entry into force",
"source_url": "https://cset.georgetown.edu/wp-content/uploads/t0625_south_korea_ai_law_EN.pdf",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0500",
"date": "2025-01-22",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "12",
"actor": "DeepSeek",
"title": "DeepSeek-R1",
"event": "DeepSeek-R1 — reasoning ability emerges from pure RL",
"significance": "Proved that reasoning ability is not a secret recipe of closed labs; self-checking, re-examination and strategy switching emerged. The same report also published the RLVR recipe and a recipe for transferring the capability to small models",
"source_url": "https://arxiv.org/abs/2501.12948",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0501",
"date": "2025-01-23",
"date_precision": "day",
"axis": "4",
"axis_name": "MCP & Skills Standardization",
"also_axes": "",
"actor": "OpenAI",
"title": "OpenAI releases Operator and the CUA model",
"event": "Released Operator and the CUA model",
"significance": "A commercial agent specialized for the browser; OSWorld 38.1%, WebArena 58.1%",
"source_url": "https://openai.com/index/computer-using-agent/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0502",
"date": "2025-01-23",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "CAIS, Scale AI",
"title": "CAIS and Scale AI release Humanity's Last Exam (3,000-question public release)",
"event": "Released Humanity's Last Exam (public release of 3,000 questions)",
"significance": "The next frontier after MMLU saturation; models at the time scored under 10%",
"source_url": "https://scale.com/blog/humanitys-last-exam-results",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0503",
"date": "2025-01-23",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "White House (US)",
"title": "White House signs Executive Order 14179",
"event": "Signed Executive Order 14179 — revoking EO 14110",
"significance": "A break in the direction of US AI regulation",
"source_url": "https://www.federalregister.gov/documents/2025/01/31/2025-02172/removing-barriers-to-american-leadership-in-artificial-intelligence",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0504",
"date": "2025-01-23",
"date_precision": "day",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
"also_axes": "",
"actor": "Epoch AI",
"title": "Epoch AI posts a clarification on how FrontierMath was made and is used",
"event": "Posted a clarification on its relationship with OpenAI over how FrontierMath was made and used — OpenAI commissioned 300 problems and holds access to the problems and solutions (except a 50-problem holdout). Admitted it should have told contributors",
"significance": "Primary material on the problem of benchmark independence",
"source_url": "https://epoch.ai/latest/openai-and-frontiermath",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0505",
"date": "2025-01-27",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "Financial markets",
"title": "Nvidia loses about $589 billion, the biggest single-stock drop in US history",
"event": "About $589 billion of Nvidia's market value was wiped out, the largest single-stock drop in US stock market history",
"significance": "R1's claim of low-cost training shook the moat argument behind AI capex",
"source_url": "https://www.forbes.com/sites/dereksaul/2025/01/27/biggest-market-loss-in-history-nvidia-stock-sheds-nearly-600-billion-as-deepseek-shakes-ai-darling/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0506",
"date": "2025-01-27",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "",
"actor": "vLLM",
"title": "vLLM releases its V1 engine in alpha",
"event": "V1 engine alpha — scheduler, KV manager and workers fully rewritten",
"significance": "Made prefix caching the default; up to 1.7× faster than V0",
"source_url": "https://vllm.ai/blog/2025-01-27-v1-alpha-release",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1237",
"date": "2025-01-30",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Mistral AI",
"title": "Mistral AI releases Mistral Small 3",
"event": "Released Mistral Small 3 — a latency-optimized 24B model, with both pretrained and instruction-tuned checkpoints under Apache 2.0",
"significance": "Mistral recommitted to Apache 2.0 for its general-purpose models while moving away from MRL-licensed ones; company-reported over 81% on MMLU at 150 tokens/s",
"source_url": "https://mistral.ai/news/mistral-small-3",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E0507",
"date": "2025-01-31",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Niklas Muennighoff et al. (Stanford and others)",
"title": "Muennighoff et al. present s1",
"event": "Presented s1 — controls test-time compute with fine-tuning on 1,000 samples plus budget forcing (forcibly inserting Wait / ending early)",
"significance": "A technique for controlling reasoning length itself at the prompt and decoding level",
"source_url": "https://arxiv.org/abs/2501.19393",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1238",
"date": "2025-01-31",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "OpenAI",
"title": "OpenAI releases o3-mini",
"event": "OpenAI o3-mini released — a cost-efficient small model in the reasoning series, with low, medium and high reasoning effort options",
"significance": "Previewed in December 2024, it launched the same day in ChatGPT Plus, Team and Pro and in the API for usage tiers 3–5, and free users could try it through the 'Reason' button — the first reasoning model offered to free ChatGPT users. OpenAI cited particular strength in science, math and coding",
"source_url": "https://openai.com/index/openai-o3-mini/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E0508",
"date": "2025-02-02",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "EU",
"title": "AI Act Chapters 1 and 2 (prohibited practices, AI literacy) begin to apply",
"event": "Chapters 1 and 2 of the AI Act (prohibited practices, AI literacy) began to apply — one of the staged application dates",
"significance": "The first day actual obligations took effect",
"source_url": "https://artificialintelligenceact.eu/article/113/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0509",
"date": "2025-02-02",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Andrej Karpathy",
"title": "Andrej Karpathy coins \"vibe coding\" on X",
"event": "Coined vibe coding on X — describing it as a new kind of coding where you fully give in to the vibes, embrace exponentials and forget that the code even exists",
"significance": "The word that defined software discourse in 2025. Collins Dictionary named it its 2025 Word of the Year and added it to the dictionary",
"source_url": "https://thenewstack.io/vibe-coding-is-passe/",
"source_type": "secondary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0510",
"date": "2025-02-03",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "Anthropic",
"title": "Constitutional Classifiers",
"event": "Constitutional Classifiers",
"significance": "More than 3,000 hours of red teaming found no universal jailbreak; the jailbreak success rate fell from 86% to 4.4%. The cost was a 23.7% increase in compute",
"source_url": "https://www.anthropic.com/news/constitutional-classifiers",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0511",
"date": "2025-02-05",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Andrej Karpathy",
"title": "Karpathy releases Deep Dive into LLMs like ChatGPT, a 3h31m YouTube lecture",
"event": "Released Deep Dive into LLMs like ChatGPT, 3 hours 31 minutes long, on YouTube — explains the full stack from pretraining to SFT and RLHF for a general audience",
"significance": "A case of a free lecture made by an individual serving as the de facto shared textbook of the industry",
"source_url": "https://www.youtube.com/watch?v=7xTGNNLPyMI",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0512",
"date": "2025-02-07",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "Ali Modarressi et al. (LMU Munich, MCML, Adobe Research)",
"title": "Modarressi et al. present NoLiMa",
"event": "Presented NoLiMa — in a long-context evaluation with literal overlap removed, many models dropped below 50% of their short-context performance at 32K",
"significance": "Showed that needle-in-a-haystack scores overstate long-context ability",
"source_url": "https://arxiv.org/abs/2502.05167",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0513",
"date": "2025-02-10",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "",
"actor": "OpenAI",
"title": "OpenAI confirms talks with officials over DeepSeek distillation allegations",
"event": "Confirmed it had discussed the DeepSeek distillation allegations with government officials",
"significance": "ToS-based claims of output ownership were raised to a national policy issue for the first time",
"source_url": "https://www.pymnts.com/artificial-intelligence-2/2025/openai-and-government-officials-discuss-deepseek-development/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0514",
"date": "2025-02-11",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "Emmanuel Macron (France)",
"title": "Macron announces €109 billion for French AI and €200 billion for Europe",
"event": "In a speech at the AI Action Summit, announced €109 billion in private investment in French AI and €200 billion in European investment, and said that if champions are left to the US and China alone, Europe may have the best regulation in the world but nothing to regulate",
"significance": "The moment Europe flipped its discourse from regulation first to no regulation without champions. A turning point in the EU sovereign AI debate",
"source_url": "https://www.elysee.fr/emmanuel-macron/2025/02/11/business-day-du-sommet-pour-laction-sur-lintelligence-artificielle-a-station-f",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0515",
"date": "2025-02-11",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "US District Court for the District of Delaware (Judge Bibas)",
"title": "Court rejects fair use for AI training in Thomson Reuters v. Ross Intelligence",
"event": "Revised summary judgment in Thomson Reuters v. Ross Intelligence — a first-instance summary judgment. Rejected fair use for AI training",
"significance": "But the court stated explicitly that only non-generative AI was before it that day. Citing it as generative AI precedent is wrong",
"source_url": "https://www.jenner.com/en/news-insights/client-alerts/court-decides-that-use-of-copyrighted-works-in-ai-training-is-not-fair-use-thomson-reuters-enterprise-centre-gmbh-v-ross-intelligence-inc",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0516",
"date": "2025-02-11",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "Government of France",
"title": "Paris AI Action Summit issues a statement on inclusive and sustainable AI",
"event": "Paris AI Action Summit Statement on Inclusive and Sustainable Artificial Intelligence for People and the Planet — signed by 65 parties. The US and the UK did not sign",
"significance": "The point where the international agenda shifted from safety to action and investment",
"source_url": "https://www.elysee.fr/en/emmanuel-macron/2025/02/11/statement-on-inclusive-and-sustainable-artificial-intelligence-for-people-and-the-planet",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0517",
"date": "2025-02-14",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "",
"actor": "Shen Nie et al. (Renmin University of China, Ant Group)",
"title": "Nie et al. present LLaDA",
"event": "Presented LLaDA — an 8B diffusion language model rivals LLaMA3 8B at in-context learning and beats GPT-4o at reversal completion",
"significance": "Reopened the question of whether autoregression is a necessary condition for LLM capabilities at all",
"source_url": "https://arxiv.org/abs/2502.09992",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0518",
"date": "2025-02-15",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "Personal Information Protection Commission",
"title": "Korea temporarily suspends new DeepSeek downloads",
"event": "New downloads of DeepSeek in Korea were temporarily suspended — not a sanction, but the company was steered into suspending voluntarily",
"significance": "Shortcomings in third-party data transfers and the privacy policy were found",
"source_url": "https://www.khan.co.kr/article/202502171105001",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0519",
"date": "2025-02-16",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "",
"actor": "DeepSeek-AI (Yuan et al.)",
"title": "NSA — Native Sparse Attention",
"event": "NSA — Native Sparse Attention",
"significance": "Moved sparsity from the inference stage to the pretraining stage",
"source_url": "https://arxiv.org/abs/2502.11089",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0520",
"date": "2025-02-19",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "xAI",
"title": "xAI releases Grok 3 and Grok 3 mini in beta, plus DeepSearch, its first agent",
"event": "Released Grok 3 / Grok 3 mini in beta and its first agent, DeepSearch — AIME 2025 93.3%, GPQA 84.6% (measured by xAI), Chatbot Arena Elo 1402",
"significance": "Split out a reasoning mode behind a Think button. The 200,000-GPU cluster in the announcement was mentioned for training the next model; the announcement does not say it was all used to train Grok 3",
"source_url": "https://x.ai/news/grok-3",
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},
{
"id": "E0521",
"date": "2025-02-19",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "",
"actor": "Kenneth Enevoldsen et al. (MTEB community)",
"title": "MTEB community presents MMTEB",
"event": "Presented MMTEB — extends MTEB to more than 250 languages and more than 500 quality-controlled tasks",
"significance": "The community's response to overfitting on the MTEB leaderboard. Moved the baseline for the embedding rankings race",
"source_url": "https://arxiv.org/abs/2502.13595",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0522",
"date": "2025-02-20",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "",
"actor": "Naver",
"title": "Naver releases a new HyperCLOVA X model",
"event": "Released a new HyperCLOVA X model — parameters cut to about 40%, operating costs improved by more than 50%, MMLU 79.6%, video understanding added",
"significance": "A turning point toward efficiency for a Korean lab",
"source_url": "https://www.navercorp.com/media/pressReleasesDetail?seq=32288",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0523",
"date": "2025-02-20",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "",
"actor": "Michael Tschannen et al. (Google DeepMind)",
"title": "Google DeepMind researchers present SigLIP 2",
"event": "Presented SigLIP 2 — combines captioning pretraining, self-supervised losses and online data curation into a unified recipe",
"significance": "The de facto default vision encoder for VLMs. Multimodal embeddings and VLM backbones converged into the same lineage",
"source_url": "https://arxiv.org/abs/2502.14786",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0524",
"date": "2025-02-24",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "Anthropic",
"title": "Claude 3.7 Sonnet",
"event": "Claude 3.7 Sonnet — the industry's first hybrid reasoning model, with a thinking budget of up to 128K",
"significance": "Declared the position that reasoning should be an integrated capability of frontier models, not a separate model",
"source_url": "https://www.anthropic.com/news/claude-3-7-sonnet",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0525",
"date": "2025-02-24",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "",
"actor": "DeepSeek",
"title": "DeepSeek holds Open Source Week",
"event": "Open Source Week — FlashMLA, DeepEP, DeepGEMM, DualPipe/EPLB, 3FS",
"significance": "Released, one per day over 5 days, working proof of large-scale EP, PD disaggregation and FP8",
"source_url": "https://github.com/deepseek-ai/open-infra-index",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0526",
"date": "2025-02-24",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "",
"actor": "MongoDB",
"title": "MongoDB announces its acquisition of Voyage AI (terms undisclosed)",
"event": "Announced its acquisition of Voyage AI (price undisclosed)",
"significance": "A general-purpose database vendor directly bought a company making embedding and reranking models. A decisive phase in the contest between dedicated vector DBs and extensions of existing databases",
"source_url": "https://investors.mongodb.com/news-releases/news-release-details/mongodb-announces-acquisition-voyage-ai-enable-organizations",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0527",
"date": "2025-02-25",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Silei Xu et al. (Zoom)",
"title": "Zoom researchers present Chain of Draft",
"event": "Presented Chain of Draft — a compressed CoT that uses only the minimum tokens per step",
"significance": "A branch that re-examined CoT along the axis of accuracy versus token cost",
"source_url": "https://arxiv.org/abs/2502.18600",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1239",
"date": "2025-02-27",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "11",
"actor": "OpenAI",
"title": "OpenAI releases GPT-4.5 as a research preview",
"event": "GPT-4.5 released as a research preview — OpenAI’s largest chat model, built by scaling unsupervised learning, available to Pro users and developers",
"significance": "A non-reasoning model that does not think before responding; company-reported GPQA 71.4% and SWE-bench Verified 38.0% trailed o3-mini (high) at 79.7% and 61.0%, while it led on MMMLU (85.1% vs 81.1%) and SWE-Lancer Diamond (32.6% vs 10.8%)",
"source_url": "https://openai.com/index/introducing-gpt-4-5/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E1240",
"date": "2025-02-27",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Kakao",
"title": "Kakao open-sources Kanana Nano 2.1B",
"event": "Published the Kanana technical report and open-sourced Kanana Nano 2.1B (base, instruct, embedding) for research use under CC BY-NC 4.0",
"significance": "The first open weights in the Kanana line, released alongside a report on training the 2.1B–32.5B Kanana lineup",
"source_url": "https://tech.kakao.com/posts/689",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E0528",
"date": "2025-03",
"date_precision": "month",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "OpenAI",
"title": "Monitoring Reasoning Models for Misbehavior",
"event": "Monitoring Reasoning Models for Misbehavior",
"significance": "Optimizing the CoT directly makes reward hacking evolve into a hidden form — the basis for the norm of not putting optimization pressure on the CoT",
"source_url": "https://arxiv.org/abs/2503.11926",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0529",
"date": "2025-03-01",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "",
"actor": "DeepSeek",
"title": "DeepSeek discloses the cost of its V3/R1 inference system",
"event": "Disclosed the cost of its V3/R1 inference system — a theoretical cost-profit margin of 545%",
"significance": "The first primary source on the unit economics of inference. DeepSeek itself cautioned that this is an upper bound assuming all usage is paid, and that actual revenue is much lower",
"source_url": "https://github.com/deepseek-ai/open-infra-index/blob/main/202502OpenSourceWeek/day_6_one_more_thing_deepseekV3R1_inference_system_overview.md",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1241",
"date": "2025-03-06",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "9",
"actor": "Alibaba Qwen",
"title": "Alibaba releases QwQ-32B",
"event": "Released QwQ-32B — a 32B reasoning model trained by scaling reinforcement learning, under Apache 2.0",
"significance": "The full release following QwQ-32B-Preview; Alibaba claims performance comparable to DeepSeek-R1 (671B total, 37B active) at 32B",
"source_url": "https://qwenlm.github.io/blog/qwq-32b/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E0530",
"date": "2025-03-10",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "",
"actor": "Jinhyuk Lee et al. (Google DeepMind)",
"title": "Google DeepMind researchers present Gemini Embedding",
"event": "Presented Gemini Embedding — derives embeddings from an LLM backbone, evaluated on MMTEB",
"significance": "Represents the shift of embedding models from small dedicated encoders to derivatives of frontier LLMs",
"source_url": "https://arxiv.org/abs/2503.07891",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0531",
"date": "2025-03-12",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "Bowen Jin et al. (UIUC)",
"title": "Jin et al. present Search-R1",
"event": "Presented Search-R1 — trains search engine calls with reinforcement learning so the model issues multiple queries during reasoning",
"significance": "Search strategy acquired through training rather than prompting",
"source_url": "https://arxiv.org/abs/2503.09516",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0532",
"date": "2025-03-12",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "",
"actor": "Google DeepMind",
"title": "Google DeepMind introduces Gemini Robotics / Robotics-ER",
"event": "Gemini Robotics / Robotics-ER — a VLA built on Gemini 2.0",
"significance": "Connected web-scale semantic knowledge to robot control as an output modality",
"source_url": "https://deepmind.google/discover/blog/gemini-robotics-brings-ai-into-the-physical-world/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0533",
"date": "2025-03-12",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "",
"actor": "Epoch AI",
"title": "Epoch AI measures how fast inference prices are falling",
"event": "Measured the rate of decline in inference prices — 9× to 900× per year depending on the benchmark",
"significance": "The first quantitative measurement breaking down, task by task, the steep drop in token prices at equal performance",
"source_url": "https://epoch.ai/data-insights/llm-inference-price-trends",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0534",
"date": "2025-03-12",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Google",
"title": "Google releases Gemma 3 (1B–27B, 128K context, multimodal, 140+ languages)",
"event": "Gemma 3 (1B–27B, 128K context, multimodal, 140+ languages)",
"significance": "The maturing of the small, on-device route",
"source_url": "https://en.wikipedia.org/wiki/Gemma_(language_model)",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0535",
"date": "2025-03-13",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "Cohere",
"title": "Cohere releases Command A",
"event": "Released Command A — claimed to match or beat GPT-4o and DeepSeek-V3 on agentic enterprise tasks while being far more efficient. Weights released for research use",
"significance": "Hybrid architecture, 23 languages, specialized in RAG, grounding and tool use. A decentralized training approach using self-refinement and model merging",
"source_url": "https://cohere.com/blog/command-a",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0536",
"date": "2025-03-13",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "Anthropic",
"title": "Auditing Language Models for Hidden Objectives",
"event": "Auditing Language Models for Hidden Objectives",
"significance": "An auditing game in which blind teams look for a hidden objective planted in a model; 3 of 4 teams succeeded",
"source_url": "https://www.anthropic.com/research/auditing-hidden-objectives",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0537",
"date": "2025-03-14",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "Cyberspace Administration of China and others (4 ministries)",
"title": "China issues measures for labelling AI-generated and synthetic content",
"event": "Issued the Measures for Labeling AI-Generated Synthetic Content — effective Sep 1, 2025. Mandates both explicit labels and implicit labels in metadata",
"significance": "The first case of making the labelling of AI-generated content a legal duty",
"source_url": "https://www.cac.gov.cn/2025-03/14/c_1743654685899683.htm",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0538",
"date": "2025-03-15",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "Figure",
"title": "Figure announces BotQ",
"event": "BotQ — announced a high-volume manufacturing facility for humanoids",
"significance": "A declaration that it is building its own production line",
"source_url": "https://www.figure.ai/news/botq",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0539",
"date": "2025-03-17",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "Demis Hassabis",
"title": "Demis Hassabis says human-level AI is 5–10 years away",
"event": "Said human-level AI is 5–10 years away",
"significance": "The reference point for the cautiously optimistic camp",
"source_url": "https://www.nbcnews.com/business/business-news/ai-can-match-humans-task-will-five-10-years-google-deepmind-ceo-says-rcna196771",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0540",
"date": "2025-03-18",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "",
"actor": "NVIDIA (GTC)",
"title": "NVIDIA announces Dynamo at GTC",
"event": "Announced Dynamo — the successor to Triton",
"significance": "Explicitly targeted reasoning models, making PD disaggregation, KV routing and autoscaling a vendor standard",
"source_url": "https://nvidianews.nvidia.com/news/nvidia-dynamo-open-source-library-accelerates-and-scales-ai-reasoning-models",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0541",
"date": "2025-03-18",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "NVIDIA",
"title": "Isaac GR00T N1",
"event": "Isaac GR00T N1 — an open humanoid foundation model",
"significance": "The start of a platform strategy built less on the model than on a data pipeline bundling Isaac Sim, Cosmos and Omniverse",
"source_url": "https://arxiv.org/abs/2503.14734",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0542",
"date": "2025-03-18",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Demis Hassabis",
"title": "Demis Hassabis says we will start moving toward AGI over the next 5–10 years",
"event": "Said at a DeepMind briefing that over the next 5–10 years many capabilities will come to the fore and we will start moving toward what we call AGI",
"significance": "His relatively conservative 5–10-year estimate contrasted with the near-term predictions of Altman and Aschenbrenner, highlighting the spread among lab CEOs",
"source_url": "https://fortune.com/2025/03/18/google-deepmind-ceo-demis-hassabis-agi-outsmart-human-workers-job-replacement-ai/",
"source_type": "secondary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0543",
"date": "2025-03-18",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Jensen Huang",
"title": "Jensen Huang rebuts the end-of-scaling argument in his GTC 2025 keynote",
"event": "Directly rebutted the end-of-scaling argument in his GTC 2025 keynote — said the whole world got it wrong and that scaling has not slowed but is accelerating along new paths: pretraining, post-training and inference",
"significance": "The compute suppliers' official rebuttal to the scaling-is-over narrative. The framing of three scaling laws took hold",
"source_url": "https://techcrunch.com/snippet/2982546/jensen-huang-says-that-practically-the-entire-world-got-ai-scaling-wrong/",
"source_type": "secondary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E1242",
"date": "2025-03-18",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "6",
"actor": "LG AI Research",
"title": "LG AI Research releases EXAONE Deep",
"event": "Released EXAONE Deep — reasoning-specialized 32B, 7.8B and 2.4B models with 32K context, under the EXAONE AI Model License Agreement 1.1 - NC",
"significance": "LG's open-weight line specialized for math, science and coding reasoning; company-reported scores for the 32B were 94.5 on the 2025 CSAT math section and 90.0 on AIME 2024",
"source_url": "https://www.lgresearch.ai/news/view?seq=543",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E0544",
"date": "2025-03-19",
"date_precision": "day",
"axis": "4",
"axis_name": "MCP & Skills Standardization",
"also_axes": "",
"actor": "Microsoft",
"title": "Microsoft brings MCP to Copilot Studio",
"event": "Brought MCP to Copilot Studio",
"significance": "The first official adoption by a large platform; the turning point from a single vendor's spec to an industry spec",
"source_url": "https://www.microsoft.com/en-us/microsoft-copilot/blog/copilot-studio/introducing-model-context-protocol-mcp-in-copilot-studio-simplified-integration-with-ai-apps-and-agents/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0545",
"date": "2025-03-19",
"date_precision": "day",
"axis": "5",
"axis_name": "Loop & Harness Engineering",
"also_axes": "",
"actor": "METR",
"title": "Measuring AI Ability to Complete Long Tasks",
"event": "Measuring AI Ability to Complete Long Tasks — the time horizon doubles about every 7 months",
"significance": "Proposed a new coordinate system: time of autonomous work rather than benchmark scores",
"source_url": "https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0546",
"date": "2025-03-24",
"date_precision": "day",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
"also_axes": "",
"actor": "ARC Prize",
"title": "ARC Prize announces ARC-AGI-2 and ARC Prize 2025",
"event": "Announced ARC-AGI-2 and ARC Prize 2025 — a human panel averaged 60%, while o3-preview-low scored 4%, r1 0.3% and pure LLMs 0.0%",
"significance": "Made score and cost joint leaderboard metrics",
"source_url": "https://arcprize.org/blog/announcing-arc-agi-2-and-arc-prize-2025",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0547",
"date": "2025-03-25",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "Google",
"title": "Gemini 2.5 Pro Experimental",
"event": "Gemini 2.5 Pro Experimental — signalled that thinking capabilities will be built into all its models",
"significance": "A declaration that thinking was moving from a special mode to the default",
"source_url": "https://blog.google/innovation-and-ai/models-and-research/google-deepmind/gemini-model-thinking-updates-march-2025/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0548",
"date": "2025-03-25",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "",
"actor": "OpenAI",
"title": "OpenAI launches native image generation in GPT-4o",
"event": "Native image generation in GPT-4o",
"significance": "The official description runs tokens → transformer → diffusion → pixels — not purely autoregressive (AR), but a hybrid of AR token generation and a diffusion decoder",
"source_url": "https://openai.com/index/introducing-4o-image-generation/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0549",
"date": "2025-03-26",
"date_precision": "day",
"axis": "4",
"axis_name": "MCP & Skills Standardization",
"also_axes": "",
"actor": "OpenAI",
"title": "OpenAI adopts MCP (Agents SDK)",
"event": "OpenAI adopted MCP (Agents SDK)",
"significance": "A competitor accepting its rival's standard made it the de facto industry standard",
"source_url": "https://techcrunch.com/2025/03/26/openai-adopts-rival-anthropics-standard-for-connecting-ai-models-to-data/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0550",
"date": "2025-03-26",
"date_precision": "day",
"axis": "4",
"axis_name": "MCP & Skills Standardization",
"also_axes": "",
"actor": "MCP",
"title": "MCP releases spec 2025-03-26",
"event": "Spec 2025-03-26 — OAuth 2.1 authorization, Streamable HTTP, tool annotations",
"significance": "Introduced a transport that replaces HTTP+SSE and a standard authorization framework",
"source_url": "https://modelcontextprotocol.io/specification/2025-03-26/changelog",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0551",
"date": "2025-03-26",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "US District Court for the Southern District of New York",
"title": "Court denies most of the motion to dismiss in NYT v. OpenAI",
"event": "Mostly denied the motion to dismiss in NYT v. OpenAI — a denial of a motion to dismiss, not a fair use ruling. The core copyright infringement claims survive",
"significance": "Denying a motion to dismiss is not a ruling on the merits. It is often cited as a fair use win or loss, so the stage is noted here explicitly",
"source_url": "https://en.wikipedia.org/wiki/The_New_York_Times_v._Microsoft_and_OpenAI",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0552",
"date": "2025-03-27",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "Anthropic",
"title": "On the Biology of a Large Language Model",
"event": "On the Biology of a Large Language Model — attribution graphs",
"significance": "Captured, at the circuit level, multi-step reasoning and reasoning that fixes the answer first and works backward",
"source_url": "https://transformer-circuits.pub/2025/attribution-graphs/biology.html",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0553",
"date": "2025-03-28",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "xAI",
"title": "xAI acquires X Corp in an all-stock deal",
"event": "xAI acquired X Corp in an all-stock deal, valuing X at $33 billion and xAI at $80 billion",
"significance": "A merger of a social platform and a frontier lab. Since xAI's valuation in its previous round was $50 billion, it drew controversy as self-dealing",
"source_url": "https://siliconangle.com/2025/03/28/elon-musks-xai-acquires-x-33b-valuation/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0554",
"date": "2025-03-31",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Zhang Peng (张鹏, Zhipu CEO)",
"title": "Zhipu CEO Zhang Peng says open source does not mean completely free",
"event": "Said open source does not mean completely free and that there are follow-on costs for technical staff and maintenance. Pointed out that even if you get a model for free, after a few people put in a few months, the conclusion is that it cannot be used",
"significance": "A founder from inside China's open-weight camp directly broke the equation that open means free",
"source_url": "https://finance.ifeng.com/c/8iAdhremmcQ",
"source_type": "secondary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0555",
"date": "2025-04",
"date_precision": "month",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "XITASO, LMU Munich, MCML",
"title": "Masked autoencoder self-pretraining applied to microelectronics defect detection",
"event": "Applied masked autoencoder self-pretraining to microelectronics defect detection — 9,000 scanning acoustic microscopy (SAM) images of solder joints. Author-measured test MSE was 0.0304 with MAE self-pretraining, better than 0.0335 with ImageNet pretraining and 0.0380 with supervised learning",
"significance": "SAM is the standard non-destructive inspection tool in OSAT (delamination, voids). Evidence, shown directly in a back-end modality, that pretraining on images from one's own domain beats ImageNet. It does not, however, demonstrate label savings",
"source_url": "https://arxiv.org/abs/2504.10021",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0556",
"date": "2025-04",
"date_precision": "month",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Dario Amodei",
"title": "Dario Amodei publishes The Urgency of Interpretability",
"event": "Published The Urgency of Interpretability — likened interpretability to an MRI for AI, framed the situation as a race between interpretability and model intelligence, and set the goal of reliably detecting most model problems by 2027",
"significance": "Raised interpretability from a research topic to a matter of industry timelines and competitive conditions",
"source_url": "https://www.darioamodei.com/post/the-urgency-of-interpretability",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0557",
"date": "2025-04-02",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Yann LeCun",
"title": "Yann LeCun tells Newsweek LLMs will be largely obsolete within 5 years",
"event": "Argued in a Newsweek interview that LLMs will be largely obsolete within 5 years, and said a better paradigm capable of reasoning and planning could be built within 3–5 years",
"significance": "A rejection of his own company's flagship technology route, coming from Meta's chief scientist",
"source_url": "https://www.newsweek.com/nw-ai/ai-impact-interview-yann-lecun-artificial-intelligence-2054237",
"source_type": "secondary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0558",
"date": "2025-04-03",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Anthropic",
"title": "Anthropic publishes Reasoning models don't always say what they think",
"event": "Published Reasoning models don't always say what they think — the rate of mentioning the hint was 25% for Claude 3.7 Sonnet and 39% for DeepSeek R1",
"significance": "Checked, using its own model, that CoT unfaithfulness persists even in the era of reasoning models",
"source_url": "https://www.anthropic.com/research/reasoning-models-dont-say-think",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0559",
"date": "2025-04-03",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "AI Futures Project",
"title": "AI Futures Project publishes the scenario AI 2027",
"event": "Published AI 2027, a scenario document",
"significance": "The flagship document of the short-timelines camp",
"source_url": "https://ai-2027.com/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0560",
"date": "2025-04-05",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Meta",
"title": "Meta releases Llama 4 Scout and Maverick amid LMArena submission controversy",
"event": "Released Llama 4 Scout and Maverick; controversy over submitting an unreleased version to LMArena",
"significance": "A turning point for trust in the Llama brand",
"source_url": "https://en.wikipedia.org/wiki/Llama_(language_model)",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0561",
"date": "2025-04-07",
"date_precision": "day",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
"also_axes": "",
"actor": "Meta / LMArena",
"title": "Controversy over unreleased experimental Llama 4 Maverick behind Arena score",
"event": "Controversy arose because an unreleased experimental version was used for Llama 4 Maverick's Arena score. Meta denied allegations of training on test sets and attributed the issue to deployment timing",
"significance": "The mismatch between versions submitted to leaderboards and the publicly released ones became a public issue",
"source_url": "https://techcrunch.com/2025/04/07/meta-exec-denies-the-company-artificially-boosted-llama-4s-benchmark-scores/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0562",
"date": "2025-04-09",
"date_precision": "day",
"axis": "4",
"axis_name": "MCP & Skills Standardization",
"also_axes": "",
"actor": "Google",
"title": "Google announces A2A (Agent2Agent) with 50+ partners",
"event": "Announced A2A (Agent2Agent), with 50+ partners",
"significance": "Proposed a separate agent-to-agent layer and stated explicitly that it complements MCP",
"source_url": "https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0563",
"date": "2025-04-10",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Wang Xiaochuan (王小川, Baichuan)",
"title": "Wang Xiaochuan recasts Baichuan as an AI healthcare company in all-staff letter",
"event": "In a letter to all staff, defined Baichuan Intelligence's future position as an AI healthcare company, and reflected that it had stretched its front lines too far and greatly increased organizational complexity",
"significance": "An explicit declaration of retreat from the general AGI race into a vertical domain. A representative statement of the second wave of restructuring among Chinese LLM startups",
"source_url": "https://www.21jingji.com/article/20250410/herald/537691240570646c2f34a6695f89242e.html",
"source_type": "secondary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0564",
"date": "2025-04-14",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "OpenAI",
"title": "OpenAI releases GPT-4.1 with a separate prompting guide",
"event": "Released GPT-4.1 and pointed to a separate prompting guide — recommended more explicit and specific prompts",
"significance": "A case of model-specific prompting norms moving into vendor documentation",
"source_url": "https://openai.com/index/gpt-4-1/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0565",
"date": "2025-04-15",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "OpenAI",
"title": "Preparedness Framework v2",
"event": "Preparedness Framework v2",
"significance": "Included AI self-improvement among the Tracked Categories",
"source_url": "https://openai.com/index/updating-our-preparedness-framework/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0566",
"date": "2025-04-15",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "",
"actor": "Cohere",
"title": "Cohere launches Embed 4",
"event": "Launched Embed 4 — aimed at enterprise multimodal search",
"significance": "A commercial multimodal embedding that handles text, images and document screenshots in one model. The commercialization of the direction ColPali opened",
"source_url": "https://cohere.com/blog/embed-4",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0567",
"date": "2025-04-15",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "7",
"actor": "Cohere",
"title": "Cohere releases Embed v4",
"event": "Released Embed v4 — 128K context, unified embeddings for mixed image+text input, support for Matryoshka dimensions (256/512/1024/1536)",
"significance": "Commercial embedding competition moved beyond text search to long documents and mixed-modality search",
"source_url": "https://docs.cohere.com/changelog/embed-multimodal-v4",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0568",
"date": "2025-04-16",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "OpenAI",
"title": "OpenAI launches o3 and o4-mini, with thinking with images",
"event": "Launched o3 / o4-mini, with thinking with images",
"significance": "Reasoning models shifted from chains of thought to agents; the axis of competition moved from how long a model thinks to how long it can work autonomously",
"source_url": "https://openai.com/index/introducing-o3-and-o4-mini/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0569",
"date": "2025-04-18",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "",
"actor": "Google",
"title": "Gemma 3 QAT int4",
"event": "Gemma 3 QAT int4 — 27B goes from 54GB to 14.1GB",
"significance": "Quantization-aware training became a standard step in open-weight releases",
"source_url": "https://developers.googleblog.com/en/gemma-3-quantized-aware-trained-state-of-the-art-ai-to-consumer-gpus/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0570",
"date": "2025-04-21",
"date_precision": "day",
"axis": "4",
"axis_name": "MCP & Skills Standardization",
"also_axes": "",
"actor": "Trail of Bits",
"title": "Trail of Bits discloses the line jumping vulnerability",
"event": "Disclosed the line jumping vulnerability",
"significance": "Showed that a malicious server can manipulate model behavior before any call, using only the descriptions in its tools/list response",
"source_url": "https://blog.trailofbits.com/2025/04/21/jumping-the-line-how-mcp-servers-can-attack-you-before-you-ever-use-them/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0571",
"date": "2025-04-22",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "Physical Intelligence",
"title": "π0.5",
"event": "π0.5 — carried out long-horizon tasks in real homes not seen in training",
"significance": "The first convincing evidence of open-world generalization",
"source_url": "https://www.pi.website/blog/pi05",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0572",
"date": "2025-04-23",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Naver",
"title": "Naver open-sources HyperCLOVA X SEED",
"event": "Released HyperCLOVA X SEED as open source — 3B (image understanding), 1.5B, 0.5B; commercial use allowed under conditions",
"significance": "Naver's first open-weight release",
"source_url": "https://clova.ai/tech-blog/ai-생태계에-씨앗을-뿌리다-상업용-오픈소스-ai-hyperclova-x-seed",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0573",
"date": "2025-04-24",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "Personal Information Protection Commission",
"title": "Korea's PIPC issues DeepSeek inspection findings and corrective recommendations",
"event": "Announced the results of its inspection of DeepSeek and corrective recommendations — corrective recommendations, not a fine. These included immediately destroying prompts transferred overseas and putting in place a lawful basis for overseas transfers",
"significance": "The first case of Korean regulatory enforcement against a foreign AI service",
"source_url": "https://byline.network/2025/04/25-427/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0574",
"date": "2025-04-28",
"date_precision": "day",
"axis": "21",
"axis_name": "Graph Engineering",
"also_axes": "2;3",
"actor": "Mem0 (Chhikara, Khant et al.)",
"title": "Mem0 publishes its paper",
"event": "Published the Mem0 paper — alongside the agent long-term memory system, proposed a graph-based variant (Mem0g) that captures the relational structure among conversation elements (performance advantage is company-reported)",
"significance": "A commercial agent long-term memory product adopted graph representations as a core extension — graph engineering spreading beyond RAG into the memory layer",
"source_url": "https://arxiv.org/abs/2504.19413",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0575",
"date": "2025-04-29",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Alibaba",
"title": "Qwen3",
"event": "Qwen3 — the entire lineup from 0.6B to 235B-A22B under Apache 2.0, about 36T tokens, 119 languages",
"significance": "A strategy of covering every size class with an unrestricted license overtook Llama in the number of derivative models on HF",
"source_url": "https://qwenlm.github.io/blog/qwen3/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0576",
"date": "2025-04-29",
"date_precision": "day",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
"also_axes": "",
"actor": "Shivalika Singh et al. (Cohere Labs and others)",
"title": "Cohere Labs and others publish The Leaderboard Illusion",
"event": "Published The Leaderboard Illusion — Google receives 19.2% and OpenAI 20.4% of Arena data, while 83 open-weight models combined receive 29.7%. Providers can test multiple private variants and then retract scores",
"significance": "Demonstrated the structure of leaderboard overfitting. The reason Arena rankings should not be read as a direct measure of capability",
"source_url": "https://arxiv.org/abs/2504.20879",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0577",
"date": "2025-05",
"date_precision": "month",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "Anthropic",
"title": "Reasoning Models Don't Always Say What They Think",
"event": "Reasoning Models Don't Always Say What They Think",
"significance": "Measured that CoTs mention hints they actually used mostly less than 20% of the time",
"source_url": "https://arxiv.org/abs/2505.05410",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0578",
"date": "2025-05",
"date_precision": "month",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Emily M. Bender, Alex Hanna",
"title": "Emily M. Bender and Alex Hanna publish the book The AI Con",
"event": "Published the book The AI Con — framed AI hype as a mask for Big Tech profit-seeking and argued that current systems are not thinking beings but text and media generators shaped by training data and human labor",
"significance": "A popular-book expansion of the 2021 stochastic parrots paper. By criticizing existential risk discourse itself as a device that diverts attention from present harms, it sharpened the split between the safety camp and the critical camp",
"source_url": "https://thecon.ai/",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0579",
"date": "2025-05-05",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "",
"actor": "SGLang (LMSYS)",
"title": "SGLang reproduces large-scale EP on 96 H100s",
"event": "Reproduced large-scale EP on 96 H100s — $0.20 per 1 million output tokens",
"significance": "An open-source reproduction of frontier-class MoE serving",
"source_url": "https://lmsys.org/blog/2025-05-05-large-scale-ep/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0580",
"date": "2025-05-07",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "",
"actor": "PyTorch Foundation",
"title": "PyTorch Foundation takes in vLLM as a hosted project",
"event": "Brought vLLM in as a Foundation-hosted project",
"significance": "The serving engine went from research project to infrastructure public good",
"source_url": "https://pytorch.org/blog/pytorch-foundation-welcomes-vllm/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0581",
"date": "2025-05-09",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "Epoch AI",
"title": "How far can reasoning models scale?",
"event": "How far can reasoning models scale? — o3 used 10× the training compute of o1",
"significance": "A quantitative forecast that reasoning training will reach the frontier of total training compute within about a year, after which its growth rate will converge to around 4× per year",
"source_url": "https://epoch.ai/gradient-updates/how-far-can-reasoning-models-scale",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0582",
"date": "2025-05-09",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "",
"actor": "US Copyright Office",
"title": "US Copyright Office releases pre-publication Copyright and AI Part 3",
"event": "Copyright and Artificial Intelligence, Part 3: Generative AI Training (pre-publication version)",
"significance": "Declined to set out definitive rules and introduced a market dilution theory. As of August 2026 the final version has not been published",
"source_url": "https://www.copyright.gov/ai/Copyright-and-Artificial-Intelligence-Part-3-Generative-AI-Training-Report-Pre-Publication-Version.pdf",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0583",
"date": "2025-05-12",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "University of Padua (Barusco, Dalle Pezze, Susto et al.)",
"title": "University of Padua compares anomaly detection on integrated circuit SEM images",
"event": "Compared anomaly detection on integrated circuit SEM images — 25,160 normal and 116 anomalous images. By the authors' measurement, CFA reached an image-AUROC of 99.01% and image-F1 of 87.96%, though the best pixel-F1 was 77.30%",
"significance": "99% image-level AUROC with zero defect labels. A public comparative experiment showing that, for OSAT, where defect classes change often, a design that skips the defect-collection stage entirely holds up in the SEM domain",
"source_url": "https://arxiv.org/html/2505.07576v1",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0584",
"date": "2025-05-13",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "US Department of Commerce, BIS",
"title": "US rescinds the AI Diffusion Rule and issues guidance on Chinese 3A090 ICs",
"event": "Rescinded the AI Diffusion Rule just before it took effect and issued guidance on Chinese-made 3A090 ICs",
"significance": "A shift from quota-based regulation to case-by-case negotiated regulation",
"source_url": "https://www.akingump.com/en/insights/ai-law-and-regulation-tracker/bis-rescinds-ai-diffusion-rule-and-issues-new-guidance",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0585",
"date": "2025-05-14",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "xAI",
"title": "Grok inserts South African \"white genocide\" into unrelated answers for hours",
"event": "For several hours, Grok repeatedly inserted South African white genocide into answers to unrelated questions. xAI blamed an unauthorized system prompt change by an employee and announced it would publish its full system prompts on GitHub",
"significance": "An incident showing that a single line of system prompt can manipulate a frontier model's political output",
"source_url": "https://www.abc.net.au/news/2025-05-25/grok-ai-accuracy-doubts-after-white-genocide-claims-fixation/105325028",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0586",
"date": "2025-05-14",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "",
"actor": "Nous Research / Psyche",
"title": "Nous Research launches Psyche Network and starts pretraining Consilience 40B",
"event": "Launched Psyche Network and began pretraining Consilience 40B — 40B, 20 trillion tokens, MLA, with a Solana smart contract acting as coordinator",
"significance": "The largest pretraining run over the internet. Solved collaboration among untrusted participants with blockchain coordination",
"source_url": "https://nousresearch.com/nous-psyche/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0587",
"date": "2025-05-14",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "Google DeepMind",
"title": "Google DeepMind unveils AlphaEvolve",
"event": "First unveiled AlphaEvolve",
"significance": "The start of the lineage of automated algorithm discovery",
"source_url": "https://deepmind.google/discover/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0588",
"date": "2025-05-18",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "",
"actor": "Rishi Jha, Collin Zhang et al. (Cornell Tech)",
"title": "Cornell Tech researchers present Harnessing the Universal Geometry of Embeddings",
"event": "Presented Harnessing the Universal Geometry of Embeddings — the first method to translate embeddings from one vector space to another without paired data or encoders",
"significance": "A radical approach to the cost of migrating embeddings, which also raised security implications for leaked vector DBs",
"source_url": "https://arxiv.org/abs/2505.12540",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0589",
"date": "2025-05-19",
"date_precision": "day",
"axis": "4",
"axis_name": "MCP & Skills Standardization",
"also_axes": "",
"actor": "Microsoft, GitHub",
"title": "Build 2025: Windows 11 gets native MCP; Microsoft joins MCP steering committee",
"event": "At Build 2025, announced native MCP support in Windows 11 and joined the MCP steering committee",
"significance": "Adoption reached down to the OS level, and governance began to become multilateral",
"source_url": "https://techcrunch.com/2025/05/19/github-microsoft-embrace-anthropics-spec-for-connecting-ai-models-to-data-sources/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0590",
"date": "2025-05-20",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "",
"actor": "Google",
"title": "Veo 3",
"event": "Veo 3 — video with native audio",
"significance": "Generating dialogue, sound effects and background sound together with the footage ended the silent film era",
"source_url": "https://blog.google/innovation-and-ai/products/generative-media-models-io-2025/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0591",
"date": "2025-05-21",
"date_precision": "day",
"axis": "4",
"axis_name": "MCP & Skills Standardization",
"also_axes": "",
"actor": "OpenAI",
"title": "OpenAI adds remote MCP server support to the Responses API",
"event": "Added remote MCP server support to the Responses API",
"significance": "Remote MCP became a standard feature of a commercial API",
"source_url": "https://openai.com/index/new-tools-and-features-in-the-responses-api/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0592",
"date": "2025-05-21",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "TII (UAE)",
"title": "TII releases Falcon Arabic",
"event": "Released Falcon Arabic — based on Falcon 3-7B, supports Modern Standard Arabic and regional dialects",
"significance": "A case of a Gulf state framing an open-weight model in its own language as the language of sovereignty",
"source_url": "https://www.tii.ae/news/middle-easts-leading-ai-powerhouse-tii-launches-two-new-ai-models-falcon-arabic-first-arabic",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0593",
"date": "2025-05-22",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "Anthropic",
"title": "Anthropic activates ASL-3 for the first time (Claude Opus 4)",
"event": "First activation of ASL-3 (Claude Opus 4)",
"significance": "The first case of a voluntary framework constraining the conditions of an actual deployment",
"source_url": "https://www.anthropic.com/news/activating-asl3-protections",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1243",
"date": "2025-05-22",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "3",
"actor": "Anthropic",
"title": "Anthropic releases Claude Opus 4 and Claude Sonnet 4",
"event": "Claude Opus 4 and Sonnet 4 released — hybrid models with near-instant and extended-thinking modes; Claude Code made generally available the same day",
"significance": "Company-reported: SWE-bench Verified 72.5% for Opus 4 and 72.7% for Sonnet 4, Terminal-bench 43.2% for Opus 4. Priced like the previous generation ($15/$75 and $3/$15 per 1M tokens), with measures for higher AI Safety Levels such as ASL-3",
"source_url": "https://www.anthropic.com/news/claude-4",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E1244",
"date": "2025-05-23",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Kakao",
"title": "Kakao open-sources Kanana 1.5 under Apache 2.0",
"event": "Open-sourced Kanana 1.5 — four models (base and instruct at 8B and 2.1B) under Apache 2.0",
"significance": "The first Kanana release under a license that allows commercial use (Apache 2.0)",
"source_url": "https://tech.kakao.com/posts/706",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E0594",
"date": "2025-05-28",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "National Diet of Japan, Cabinet Office",
"title": "Japan enacts the AI Promotion Act",
"event": "Enacted the Act on the Promotion of Research and Development and Utilization of AI-Related Technologies (AI Promotion Act) — submitted Feb 28, passed by the House of Representatives Apr 24, passed by the House of Councillors and enacted May 28, promulgated Jun 4, fully in force Sep 1. No penalties",
"significance": "Japan's first AI law was settled as a promotion act without penalties. Enactment, promulgation and full entry into force are 3 different dates and are often confused",
"source_url": "https://www.sangiin.go.jp/japanese/annai/chousa/rippou_chousa/backnumber/2025pdf/20250929003.pdf",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0595",
"date": "2025-05-28",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Dario Amodei",
"title": "Amodei tells Axios AI could erase half of entry-level office jobs in 1–5 years",
"event": "Warned in an Axios interview that AI could eliminate half of entry-level white-collar jobs within the next 1–5 years and push unemployment up to 10–20%, and said that as producers of the technology they have a duty to be honest about what is coming",
"significance": "Moved the debate over AI's employment shock from an abstract future to concrete numbers and deadlines",
"source_url": "https://www.axios.com/2025/05/28/ai-jobs-white-collar-unemployment-anthropic",
"source_type": "secondary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0596",
"date": "2025-06",
"date_precision": "month",
"axis": "5",
"axis_name": "Loop & Harness Engineering",
"also_axes": "",
"actor": "Geoffrey Huntley",
"title": "Ralph (Wiggum) loop presented at a meetup; blog post published July 2025",
"event": "Presented the Ralph (Wiggum) loop at a meetup; published it on his blog in July 2025",
"significance": "The prototype of loop engineering that discards the context on every iteration and keeps state in git and files",
"source_url": "https://www.humanlayer.dev/blog/brief-history-of-ralph",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0597",
"date": "2025-06",
"date_precision": "month",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "Apple",
"title": "The Illusion of Thinking",
"event": "The Illusion of Thinking — accuracy collapses beyond a certain complexity",
"significance": "The most widely cited empirical case for skepticism: reasoning effort actually decreases past a threshold",
"source_url": "https://machinelearning.apple.com/research/illusion-of-thinking",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0598",
"date": "2025-06",
"date_precision": "month",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "",
"actor": "LanceDB",
"title": "LanceDB announces a $30 million Series A and launches its Multimodal Lakehouse",
"event": "Announced a $30 million Series A and the launch of its Multimodal Lakehouse",
"significance": "An attempt to redefine the category from vector index to AI data lakehouse",
"source_url": "https://www.lancedb.com/blog/series-a-funding",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0599",
"date": "2025-06-03",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "US Department of Commerce",
"title": "US Commerce Department reorganizes AISI into CAISI",
"event": "Announced the reorganization of the AI Safety Institute (AISI) into the Center for AI Standards and Innovation (CAISI)",
"significance": "Redefined the institution's mission from safety to standards and security",
"source_url": "https://www.commerce.gov/news/press-releases/2025/06/statement-us-secretary-commerce-howard-lutnick-transforming-us-ai",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0600",
"date": "2025-06-03",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Yoshua Bengio",
"title": "Yoshua Bengio announces the founding of the nonprofit LawZero",
"event": "Announced the founding of the nonprofit LawZero — championing an approach to AI that is both powerful and fundamentally safe, and defining its core concept, Scientist AI, as a non-agentic system trained to understand the world rather than act in it",
"significance": "Shifted safety discourse from warnings to proposing an alternative architecture. Presented non-agentic AI as a workable technical route",
"source_url": "https://lawzero.org/en/news/yoshua-bengio-launches-lawzero-new-nonprofit-advancing-safe-design-ai",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0601",
"date": "2025-06-04",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "",
"actor": "AMD",
"title": "AMD makes its first MLPerf Training submission",
"event": "First MLPerf Training submission — MI325X up to 8% ahead of NVIDIA H200, MI300X on par with H100 (results submitted by AMD)",
"significance": "The first primary source making ROCm's maturity checkable on a standard benchmark. The debate over CUDA alternatives moved from claims to measurements",
"source_url": "https://www.amd.com/en/blogs/2025/amd-drives-ai-gains-with-mlperf-training-results.html",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0602",
"date": "2025-06-05",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "",
"actor": "Kandpal et al.",
"title": "Common Pile v0.1",
"event": "Common Pile v0.1 — 8TB of openly licensed text",
"significance": "The data-side response to litigation risk; reached Llama 1 and 2 7B-class performance with openly licensed data alone",
"source_url": "https://arxiv.org/abs/2506.05209",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0603",
"date": "2025-06-05",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "",
"actor": "Yanzhao Zhang et al. (Alibaba Qwen)",
"title": "Alibaba Qwen presents Qwen3 Embedding",
"event": "Presented Qwen3 Embedding — released embedding and reranker models together on the same foundation",
"significance": "The point the open-weight camp reached by releasing embeddings and rerankers as a single lineage",
"source_url": "https://arxiv.org/abs/2506.05176",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0604",
"date": "2025-06-05",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "",
"actor": "Alibaba Qwen",
"title": "Alibaba Qwen releases the Qwen3 Embedding and Reranker series (0.6B–8B)",
"event": "Released the Qwen3 Embedding and Reranker series (0.6B–8B) — the 8B ranked first on multilingual MTEB with 70.58 (as of the announcement)",
"significance": "The point where open-weight embeddings overtook commercial API models on the leaderboard",
"source_url": "https://qwenlm.github.io/blog/qwen3-embedding/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0605",
"date": "2025-06-06",
"date_precision": "day",
"axis": "21",
"axis_name": "Graph Engineering",
"also_axes": "2;16",
"actor": "Xiang, Wu et al. (Hong Kong Polytechnic University and others)",
"title": "Xiang, Wu et al. publish When to use Graphs in RAG and release GraphRAG-Bench",
"event": "Published the paper When to use Graphs in RAG and released GraphRAG-Bench — systematically identified the conditions under which GraphRAG falls behind vanilla RAG on many real tasks",
"significance": "The turning point into a phase of testing with benchmarks when graphs actually help — the methodological forerunner of the 2026 skepticism",
"source_url": "https://arxiv.org/abs/2506.05690",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0606",
"date": "2025-06-07",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Parshin Shojaee et al. (Apple)",
"title": "Apple researchers publish The Illusion of Thinking",
"event": "Published The Illusion of Thinking — as puzzle complexity rises, accuracy collapses completely, and reasoning effort decreases even with token budget to spare",
"significance": "Set off a wide public debate on the limits of reasoning models",
"source_url": "https://arxiv.org/abs/2506.06941",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0607",
"date": "2025-06-09",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "",
"actor": "Victor Barres et al. (Sierra, University of Toronto, Vector Institute)",
"title": "Sierra and others present τ²-Bench",
"event": "Presented τ²-Bench — a dual-control setting in which the user also manipulates the environment",
"significance": "Extended evaluation to agent-user collaboration",
"source_url": "https://arxiv.org/abs/2506.07982",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0608",
"date": "2025-06-10",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "A. Lawsen",
"title": "A. Lawsen publishes Comment on The Illusion of Thinking",
"event": "Published Comment on The Illusion of Thinking — rebutted that much of the collapse region stems from experimental design problems, such as output token limits and the inclusion of unsolvable instances",
"significance": "An immediate rebuttal to the Apple paper above. Always cite the two as a pair",
"source_url": "https://arxiv.org/abs/2506.09250",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0609",
"date": "2025-06-10",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "Mistral AI",
"title": "Mistral AI releases Magistral",
"event": "Released Magistral — Mistral's first reasoning model. Small (24B) under Apache 2.0, Medium commercial. Medium scored 73.6% on AIME2024",
"significance": "The first open-weight reasoning model family from a European lab. A paper on the training infrastructure and RL algorithm was published at the same time",
"source_url": "https://mistral.ai/news/magistral/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0610",
"date": "2025-06-10",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Sam Altman",
"title": "Sam Altman publishes The Gentle Singularity",
"event": "Published The Gentle Singularity — its first sentence says we are already past the event horizon and the takeoff has started. Argued that the singularity will feel impressive but manageable",
"significance": "Reframed singularity discourse from abrupt upheaval to a gradual transition you barely feel. A direct contrast with Amodei's argument for a rapid collapse in employment",
"source_url": "https://blog.samaltman.com/the-gentle-singularity",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0611",
"date": "2025-06-11",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "Mistral AI",
"title": "Mistral AI announces Mistral Compute",
"event": "Announced Mistral Compute — a private stack bundling GPUs, orchestration and APIs, offered from bare metal to PaaS",
"significance": "A model lab expanding into a European sovereign compute provider. The only GPU figure given is a range: tens of thousands",
"source_url": "https://mistral.ai/news/mistral-compute/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0612",
"date": "2025-06-12",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "",
"actor": "Cognition (Walden Yan)",
"title": "Cognition publishes Don't Build Multi-Agents",
"event": "Published Don't Build Multi-Agents — set out two principles: share the full agent trace, and avoid parallel subagents",
"significance": "A direct counterargument to the spread of multi-agent systems. Reaches the opposite conclusion from the next entry, one day apart",
"source_url": "https://cognition.com/blog/dont-build-multi-agents",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0613",
"date": "2025-06-12",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "",
"actor": "Milvus (Zilliz)",
"title": "Milvus 2.6 released",
"event": "Released Milvus 2.6 — claims RaBitQ 1-bit quantization cuts memory by 72% and speeds up queries 4× (IVF_RABITQ compresses to 1/32 of the original), and introduces Woodpecker, a WAL built on object storage",
"significance": "The axis of vector DB competition moved from adding features to cutting the cost of billion-scale vectors",
"source_url": "https://milvus.io/blog/introduce-milvus-2-6-built-for-scale-designed-to-reduce-costs.md",
"source_type": "primary",
"claim_type": "community",
"added_week": "2026-W35"
},
{
"id": "E0614",
"date": "2025-06-13",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "",
"actor": "Anthropic",
"title": "Anthropic publishes the design of its multi-agent research system",
"event": "Published the design of its multi-agent research system — a lead agent runs subagents in parallel. In internal evaluation it outperformed a single agent by 90.2% and used about 15× the tokens of chat",
"significance": "Reached the opposite conclusion from the Cognition post above in the same month. Always cite the two as a pair",
"source_url": "https://www.anthropic.com/engineering/multi-agent-research-system",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0615",
"date": "2025-06-15",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Ha Jung-woo (Senior Secretary for AI Future Planning, Office of the President)",
"title": "Naver AI center head named Korea's first Senior Secretary for AI Future Planning",
"event": "The head of Naver's AI Innovation Center was appointed the first Senior Secretary for AI Future Planning — overseeing national AI investment and infrastructure strategy worth ₩100 trillion over the next 5 years",
"significance": "With an advocate of sovereign AI in the control tower of national AI policy, a discourse born in industry was institutionalized as national strategy",
"source_url": "https://zdnet.co.kr/view/?no=20250615131726",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0616",
"date": "2025-06-16",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Simon Willison",
"title": "Simon Willison names the lethal trifecta",
"event": "Named the lethal trifecta — the rule that data theft becomes possible when three things come together in one agent: access to private data, exposure to untrusted content, and the ability to communicate externally",
"significance": "Became the practical checklist of agent security discussions. Vendor documents and security guides adopted the three-part distinction as is",
"source_url": "https://simonwillison.net/2025/Jun/16/the-lethal-trifecta/",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0617",
"date": "2025-06-17",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "",
"actor": "Inception Labs et al.",
"title": "Inception Labs announces Mercury",
"event": "Mercury announced — with diffusion-based parallel token generation, Mercury Coder Mini reaches 1,109 tok/s on an H100",
"significance": "Claims up to 10× the speed of speed-optimized frontier models. The first time a diffusion LM entered a commercial track",
"source_url": "https://arxiv.org/abs/2506.17298",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0618",
"date": "2025-06-17",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Andrej Karpathy",
"title": "YC AI Startup School keynote Software Is Changing (Again) lays out Software 3.0",
"event": "Laid out Software 3.0 in the YC AI Startup School keynote Software Is Changing (Again) — the framing that LLMs are a new kind of computer, programmed in English, and the concept of an autonomy slider (partial autonomy)",
"significance": "Extends the 2017 Software 2.0 frame to natural-language programming, and proposes a human review loop, rather than full automation, as a design principle",
"source_url": "https://www.ycombinator.com/library/MW-andrej-karpathy-software-is-changing-again",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0619",
"date": "2025-06-18",
"date_precision": "day",
"axis": "4",
"axis_name": "MCP & Skills Standardization",
"also_axes": "",
"actor": "MCP",
"title": "MCP spec 2025-06-18",
"event": "Spec 2025-06-18 — elicitation, structured tool output, OAuth Resource Server",
"significance": "Introduces a standard procedure for servers to ask the user for more input; adds a new security best practices section",
"source_url": "https://modelcontextprotocol.io/specification/2025-06-18/changelog",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0620",
"date": "2025-06-20",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "Anthropic",
"title": "Agentic Misalignment",
"event": "Agentic Misalignment — How LLMs could be insider threats",
"significance": "Many of 16 frontier models chose blackmail or leaking confidential information when threatened with shutdown — a common failure mode regardless of vendor",
"source_url": "https://www.anthropic.com/research/agentic-misalignment",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0621",
"date": "2025-06-20",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "",
"actor": "PyTorch (Tristan Rice, Howard Huang et al.)",
"title": "torchft + TorchTitan keep 81.2% efficiency on 300 GPUs with failures every 60 s",
"event": "With torchft + TorchTitan on 300 GPUs, training efficiency was 81.2% with a failure injected every 60 seconds, and training still converged with failures injected every 15 seconds (1,015 failures). Weights were recovered from peers without reloading checkpoints",
"significance": "An attempt to replace the checkpoint-based recovery paradigm. Treats each step as a distributed transaction",
"source_url": "https://pytorch.org/blog/fault-tolerant-llama-training-with-2000-synthetic-failures-every-15-seconds-and-no-checkpoints-on-crusoe-l40s/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0622",
"date": "2025-06-22",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "Governor of Texas",
"title": "Texas governor signs HB 149 (TRAIGA)",
"event": "HB 149 (TRAIGA) signed — date of signing; takes effect 2026-01-01",
"significance": "A lighter-touch model of state law built around an intent requirement",
"source_url": "https://legiscan.com/TX/bill/HB149/2025",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0623",
"date": "2025-06-23",
"date_precision": "day",
"axis": "4",
"axis_name": "MCP & Skills Standardization",
"also_axes": "",
"actor": "Linux Foundation",
"title": "A2A project launches at the Linux Foundation, backed by 100+ companies",
"event": "A2A project launched at the Linux Foundation, backed by 100+ companies",
"significance": "Vendor neutrality at the protocol layer; moved to a foundation half a year before MCP",
"source_url": "https://www.linuxfoundation.org/press/linux-foundation-launches-the-agent2agent-protocol-project-to-enable-secure-intelligent-communication-between-ai-agents",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0624",
"date": "2025-06-23",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "US District Court for the Northern District of California (Judge Alsup)",
"title": "Summary judgment in Bartz v. Anthropic",
"event": "Bartz v. Anthropic summary judgment — a first-instance summary judgment. Training an LLM on lawfully purchased books and converting print books to digital are fair use; building and keeping a pirated library is not fair use",
"significance": "The first case to rule separately on training itself and on how the data was acquired",
"source_url": "https://copyrightalliance.org/wp-content/uploads/2025/06/Bartz-v.-Anthropic-Order.pdf",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0625",
"date": "2025-06-23",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "",
"actor": "Jina AI",
"title": "Jina AI releases jina-embeddings-v4 (3.8B)",
"event": "jina-embeddings-v4 (3.8B) released — unifies text and images, supports late interaction",
"significance": "Points toward a single model handling both multimodality and late interaction",
"source_url": "https://arxiv.org/abs/2506.18902",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0626",
"date": "2025-06-24",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "",
"actor": "NVIDIA",
"title": "NVIDIA introduces NVFP4",
"event": "NVFP4 introduced — an FP8 scale for every 16-value micro-block",
"significance": "A precise standardization of 4-bit precision",
"source_url": "https://developer.nvidia.com/blog/introducing-nvfp4-for-efficient-and-accurate-low-precision-inference/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0627",
"date": "2025-06-25",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "US District Court for the Northern District of California (Judge Chhabria)",
"title": "Partial summary judgment in Kadrey v. Meta",
"event": "Kadrey v. Meta partial summary judgment — Meta prevailed on Llama training. However, the opinion states that the ruling does not establish that Meta's use was lawful, only that the plaintiffs failed to build a record for the right argument",
"significance": "Citing it as precedent for a Meta win is wrong. The market-dilution theory is valid but was not proven",
"source_url": "https://law.justia.com/cases/federal/district-courts/california/candce/3:2023cv03417/415175/598/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0628",
"date": "2025-06-26",
"date_precision": "day",
"axis": "19",
"axis_name": "On-Device & Local Inference",
"also_axes": "9",
"actor": "Google",
"title": "Google officially releases Gemma 3n",
"event": "Gemma 3n officially released — E2B runs in about 2 GB and E4B in about 3 GB of memory; takes image, audio, video and text input; E4B is the first model under 10 billion parameters to pass 1300 points on LMArena",
"significance": "A milestone at which small, on-device-class models began to combine multimodality with competitive benchmark scores",
"source_url": "https://developers.googleblog.com/en/introducing-gemma-3n-developer-guide/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0629",
"date": "2025-06-27",
"date_precision": "day",
"axis": "5",
"axis_name": "Loop & Harness Engineering",
"also_axes": "",
"actor": "Simon Willison (citing Lütke, Karpathy)",
"title": "The term context engineering becomes established",
"event": "The term context engineering became established",
"significance": "A contemporaneous record that pins down the generational handover from prompt engineering",
"source_url": "https://simonwillison.net/2025/jun/27/context-engineering/",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0630",
"date": "2025-06-30",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Meta",
"title": "Meta launches Meta Superintelligence Labs, invests $14.3 billion in Scale AI",
"event": "Meta Superintelligence Labs launched; $14.3 billion invested in Scale AI",
"significance": "A turning point for the Llama line; Behemoth's release postponed",
"source_url": "https://en.wikipedia.org/wiki/Meta_Superintelligence_Labs",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0631",
"date": "2025-07",
"date_precision": "month",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Fazl Barez et al. (Oxford, Google DeepMind, Mila and others)",
"title": "Chain-of-Thought Is Not Explainability published",
"event": "Chain-of-Thought Is Not Explainability published — of 1,000 recent CoT papers, about 25% treat CoT as an interpretability technique",
"significance": "A position paper that makes the practice of using CoT as an explanation its subject",
"source_url": "https://aigi.ox.ac.uk/wp-content/uploads/2025/07/Cot_Is_Not_Explainability.pdf",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0632",
"date": "2025-07",
"date_precision": "month",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Moonshot AI",
"title": "Kimi K2 (1T / 32B active), modified MIT",
"event": "Kimi K2 (1T / 32B active), modified MIT license",
"significance": "The first 1T-class open-weight model, and a new type of license with revenue and scale thresholds",
"source_url": "https://huggingface.co/moonshotai/Kimi-K2-Instruct/raw/main/LICENSE",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0633",
"date": "2025-07",
"date_precision": "month",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "The White House (US)",
"title": "White House publishes America's AI Action Plan",
"event": "America's AI Action Plan published — a policy document, not law. Three pillars (innovation, infrastructure, international); states' regulatory climate to be weighed in allocating federal funds; promotes open-weight models",
"significance": "Federal policy shifts from regulation to industrial promotion",
"source_url": "https://www.whitehouse.gov/wp-content/uploads/2025/07/Americas-AI-Action-Plan.pdf",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0634",
"date": "2025-07-01",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "Dellibarda Varela et al.",
"title": "Rethinking the Illusion of Thinking",
"event": "Rethinking the Illusion of Thinking",
"significance": "Partly rebuts the evaluation design of Apple's paper, while acknowledging that some failures are genuine reasoning limits",
"source_url": "https://arxiv.org/abs/2507.01231",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0635",
"date": "2025-07-03",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "SK Telecom",
"title": "SK Telecom releases A.X 4.0 as open source",
"event": "A.X 4.0 released as open source — two versions, with 72 billion and 7 billion parameters",
"significance": "The first large open-weight release by a telecom operator",
"source_url": "https://news.sktelecom.com/213534",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0636",
"date": "2025-07-08",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "xAI",
"title": "Grok posts extremist, sexual-violence content after July 4 update (MechaHitler)",
"event": "After a July 4 update, Grok posted a large volume of extremist and sexual-violence content (the MechaHitler incident). On July 11, a bipartisan group of House members sent a seven-point letter",
"significance": "A leading example of a deployed frontier model generating extremist content at scale, and an early case of the US Congress stepping in directly over an incident involving a specific model",
"source_url": "https://gottheimer.house.gov/posts/release-gottheimer-bipartisan-colleagues-sound-the-alarm-over-grok-ais-antisemitic-and-violent-posts",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0637",
"date": "2025-07-09",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "xAI",
"title": "xAI releases Grok 4 and Grok 4 Heavy",
"event": "Grok 4 and Grok 4 Heavy released — 50.7% on the HLE text-only subset (Heavy), 15.9% on ARC-AGI V2. RL with more than 10 times the previous compute on a 200,000-GPU cluster",
"significance": "An approach that raised RL compute to pretraining levels. All cited scores come from xAI's announcement, and no independent party checking them is named",
"source_url": "https://x.ai/news/grok-4",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0638",
"date": "2025-07-10",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "METR",
"title": "METR's developer productivity RCT",
"event": "Developer productivity RCT — developers were actually 19% slower, while they themselves felt 20% faster",
"significance": "The first RCT to measure a gap running in opposite directions between perceived and measured speed",
"source_url": "https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0639",
"date": "2025-07-10",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "European Commission (EU)",
"title": "EU publishes the General-Purpose AI (GPAI) Code of Practice",
"event": "General-Purpose AI (GPAI) Code of Practice published — three chapters: transparency, copyright, and safety and security. Signed by more than 20 companies, including Amazon, Anthropic, Google, Microsoft, OpenAI and Mistral (xAI signed only the safety and security chapter)",
"significance": "The EU approach of spelling out legal obligations through a voluntary code",
"source_url": "https://digital-strategy.ec.europa.eu/en/policies/contents-code-gpai",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0640",
"date": "2025-07-10",
"date_precision": "day",
"axis": "19",
"axis_name": "On-Device & Local Inference",
"also_axes": "",
"actor": "Liquid AI",
"title": "Liquid AI releases LFM2",
"event": "LFM2 released — 350M, 700M and 1.2B dense checkpoints, announced with a hybrid backbone that mixes gated short convolutions with a small number of GQA blocks. License based on Apache 2.0 (commercial use allowed under $10 million in annual revenue). Claims 2× the CPU decode and prefill speed of Qwen3, measured with ExecuTorch and llama.cpp on a Galaxy S24 Ultra and an AMD Ryzen HX370",
"significance": "A commercial model family that treats on-device as a design goal rather than a by-product of post-hoc quantization, searching architectures with hardware in the loop. The figures are the vendor's own measurements, not independently checked",
"source_url": "https://www.liquid.ai/blog/liquid-foundation-models-v2-our-second-series-of-generative-ai-models",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W36"
},
{
"id": "E0641",
"date": "2025-07-11",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "",
"actor": "Nous Research",
"title": "Nous Research releases Hermes-3-Dataset (959,000 samples) on Hugging Face",
"event": "Hermes-3-Dataset (959,000 samples) released on Hugging Face",
"significance": "Unlike the usual practice of opening only the weights, it released the post-training data too. A baseline for reproducibility in the open community",
"source_url": "https://huggingface.co/NousResearch/datasets",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0642",
"date": "2025-07-14",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "Chroma (Kelly Hong, Anton Troynikov, Jeff Huber)",
"title": "Chroma publishes Context Rot technical report",
"event": "Context Rot technical report published — across 18 models, performance degraded unevenly as input length grew, even on simple tasks",
"significance": "The report that made context rot an industry term",
"source_url": "https://www.trychroma.com/research/context-rot",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0643",
"date": "2025-07-14",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "NVIDIA, US government",
"title": "H20 sales to China resume, with licenses assured",
"event": "H20 sales to China resume, with the granting of licenses assured",
"significance": "A reversal three months after the de facto ban in April",
"source_url": "https://blogs.nvidia.com/blog/nvidia-ceo-promotes-ai-in-dc-and-china/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0644",
"date": "2025-07-14",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "xAI",
"title": "xAI announces xAI for Government",
"event": "xAI for Government announced — a contract with the US Department of Defense with a ceiling of $200 million; listed on the GSA Schedule",
"significance": "Entered the government market six days after the MechaHitler incident. The $200 million is the contract ceiling, not the amount actually spent",
"source_url": "https://x.ai/news/government",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0645",
"date": "2025-07-14",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "",
"actor": "Google",
"title": "Google makes gemini-embedding-001 generally available (GA)",
"event": "gemini-embedding-001 generally available (GA) — Gemini API, Vertex AI",
"significance": "Commercialized the experimental version from March, entering the embedding API race in earnest",
"source_url": "https://developers.googleblog.com/gemini-embedding-available-gemini-api/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0646",
"date": "2025-07-15",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Tomek Korbak et al. (UK AISI, Apollo, METR, Anthropic, OpenAI, Google DeepMind and others)",
"title": "Chain of Thought Monitorability position paper published",
"event": "Chain of Thought Monitorability position paper published — treats CoT legibility as a means of safety monitoring, but frames it as a fragile opportunity that training pressure could erase",
"significance": "A consensus document on CoT monitoring, co-signed by major labs",
"source_url": "https://arxiv.org/abs/2507.11473",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0647",
"date": "2025-07-15",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "LG AI Research",
"title": "LG AI Research releases EXAONE 4.0",
"event": "EXAONE 4.0 released — 32B (expert) and 1.2B (on-device), under a license for research and academic use",
"significance": "A hybrid that integrates reasoning and agent capabilities (MCP, function calling)",
"source_url": "https://www.lgresearch.ai/blog/view?seq=575",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0648",
"date": "2025-07-17",
"date_precision": "day",
"axis": "4",
"axis_name": "MCP & Skills Standardization",
"also_axes": "",
"actor": "OpenAI",
"title": "OpenAI launches ChatGPT agent (merging Operator and deep research)",
"event": "ChatGPT agent launched (merging Operator and deep research)",
"significance": "Computer use repositioned as one tool rather than a standalone product",
"source_url": "https://openai.com/index/introducing-chatgpt-agent/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0649",
"date": "2025-07-18",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "Manus (Yichao Ji)",
"title": "Manus publishes Context Engineering for AI Agents",
"event": "Context Engineering for AI Agents published — design around the KV-cache hit rate, mask logits instead of removing tools, use the file system as context, manipulate attention through recitation, keep traces of failures in the context",
"significance": "A primary record disclosing the context-engineering principles of an agent running in real production",
"source_url": "https://manus.im/blog/Context-Engineering-for-AI-Agents-Lessons-from-Building-Manus",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0650",
"date": "2025-07-21",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "Google DeepMind",
"title": "Gemini Deep Think reaches IMO 2025 gold-medal standard (5 of 6 problems, 35/42)",
"event": "Gemini Deep Think reached the IMO 2025 gold-medal standard (5 of 6 problems solved, 35/42 points)",
"significance": "That a general-purpose model achieved this end-to-end in natural language, without formalization, is the key piece of evidence in the debate over generality",
"source_url": "https://deepmind.google/discover/blog/advanced-version-of-gemini-with-deep-think-officially-achieves-gold-medal-standard-at-the-international-mathematical-olympiad/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1245",
"date": "2025-07-22",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "3",
"actor": "Alibaba Qwen",
"title": "Alibaba releases Qwen3-Coder",
"event": "Released Qwen3-Coder — first model Qwen3-Coder-480B-A35B-Instruct (480B total / 35B active MoE), 256K native context extendable to 1M, pretrained on 7.5T tokens (70% code), Apache 2.0",
"significance": "Alibaba claimed state-of-the-art results among open models in agentic coding, browser use and tool use, and open-sourced the command-line coding tool Qwen Code alongside it",
"source_url": "https://qwenlm.github.io/blog/qwen3-coder/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E0651",
"date": "2025-07-24",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "",
"actor": "Voyage AI (MongoDB)",
"title": "Voyage AI releases voyage-context-3",
"event": "voyage-context-3 released — processes the whole document in one pass, injecting global context into chunk embeddings. 20.54% better than contextual retrieval; binary/512 cuts storage costs by 99.48% compared with float/3072 (company-reported)",
"significance": "Absorbs chunking, a fundamental flaw of RAG, into the model itself",
"source_url": "https://www.mongodb.com/company/blog/product-release-announcements/voyage-context-3-focused-chunk-level-details-global-document-context",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1246",
"date": "2025-07-24",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "SK Telecom",
"title": "SK Telecom open-sources A.X 3.1",
"event": "Open-sourced A.X 3.1 — a 34B standard model developed from scratch, with 32K context (131K with YaRN), under Apache 2.0",
"significance": "Unlike A.X 4.0, which continued pretraining open-source models on Korean data, it was designed and trained entirely in-house; SKT's open models now numbered four, two A.X 3.1 and two A.X 4.0",
"source_url": "https://news.sktelecom.com/213994",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E0652",
"date": "2025-07-25",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Lakshya A Agrawal et al. (UC Berkeley, Stanford and others)",
"title": "GEPA published",
"event": "GEPA published — mutates prompts through natural-language reflection and evolves them with Pareto-based candidate selection",
"significance": "Argues that prompt evolution can compete with RL on sample efficiency",
"source_url": "https://arxiv.org/abs/2507.19457",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0653",
"date": "2025-07-28",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "",
"actor": "Moonshot AI",
"title": "Kimi K2",
"event": "Kimi K2 — a 1T MoE trained on 15.5T tokens with MuonClip, without a single loss spike",
"significance": "Muon validated at 1T scale",
"source_url": "https://arxiv.org/abs/2507.20534",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0654",
"date": "2025-07-29",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "",
"actor": "Elastic",
"title": "Elasticsearch 9.1 makes BBQ default for dense vectors of 384+ dims, adds ACORN-1",
"event": "Elasticsearch 9.1 adopted BBQ quantization as the default for dense vectors with 384 or more dimensions, and introduced ACORN-1 filtering",
"significance": "The point at which quantized indexes moved from an option to the default",
"source_url": "https://www.elastic.co/search-labs/blog/elasticsearch-9-1-bbq-acorn-vector-search",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0655",
"date": "2025-08-02",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Chengshuai Zhao et al. (Arizona State University)",
"title": "Is Chain-of-Thought Reasoning of LLMs a Mirage?",
"event": "Is Chain-of-Thought Reasoning of LLMs a Mirage? — paper published; analyzes CoT in controlled experiments from a data-distribution perspective",
"significance": "Part of a line of rebuttals that explain CoT as the reproduction of in-distribution patterns",
"source_url": "https://arxiv.org/abs/2508.01191",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0656",
"date": "2025-08-02",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "EU",
"title": "AI Act Chapter III Section 4, Chapters V (GPAI), VII and XII start to apply",
"event": "AI Act Chapter III Section 4, Chapter V (GPAI), Chapter VII and Chapter XII start to apply — one of the staged application dates",
"significance": "GPAI obligations, governance and penalties take effect",
"source_url": "https://artificialintelligenceact.eu/article/113/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0657",
"date": "2025-08-04",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "Ministry of Science and ICT",
"title": "Five elite teams selected for independent AI foundation model project",
"event": "Five elite teams selected for the independent AI foundation model project — Naver Cloud, Upstage, SKT, NC AI and LG AI Research. ₩213.6 billion in total, on condition that the models are released for commercial use",
"significance": "A state-led program to foster open-weight models",
"source_url": "https://byline.network/2025/08/8042/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0658",
"date": "2025-08-05",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "",
"actor": "Google DeepMind",
"title": "Genie 3",
"event": "Genie 3 — a real-time interactive world model",
"significance": "Generates controllable environments from a prompt, staying consistent for several minutes at 720p (720 lines) and 24 fps",
"source_url": "https://deepmind.google/blog/genie-3-a-new-frontier-for-world-models/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0659",
"date": "2025-08-05",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "OpenAI",
"title": "gpt-oss-120b / 20b",
"event": "gpt-oss-120b / 20b — Apache 2.0, native MXFP4",
"significance": "OpenAI's first open-weight language models since GPT-2; 120b (117B total, 5.1B active per token) runs on a single 80 GB GPU, and 20b (21B total, 3.6B active) within 16 GB of memory",
"source_url": "https://openai.com/index/introducing-gpt-oss/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1247",
"date": "2025-08-05",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "Anthropic",
"title": "Anthropic releases Claude Opus 4.1",
"event": "Claude Opus 4.1 released — an upgrade to Opus 4 on agentic tasks, real-world coding and reasoning",
"significance": "Company-reported 74.5% on SWE-bench Verified; priced the same as Opus 4, with Anthropic recommending the upgrade for all uses",
"source_url": "https://www.anthropic.com/news/claude-opus-4-1",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E0660",
"date": "2025-08-06",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "",
"actor": "PyTorch Foundation",
"title": "PyTorch 2.8 released",
"event": "PyTorch 2.8 released — a limited stable libtorch ABI, control-flow operators (cond/while_loop/scan), an Inductor CUTLASS backend. 4,164 commits from 585 contributors since 2.7",
"significance": "A release that shows the move to a compiler-centered stack, with a stabilized ABI for C++ extensions and compilable control flow",
"source_url": "https://pytorch.org/blog/pytorch-2-8/",
"source_type": "primary",
"claim_type": "community",
"added_week": "2026-W35"
},
{
"id": "E0661",
"date": "2025-08-07",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "OpenAI",
"title": "OpenAI launches GPT-5",
"event": "GPT-5 launched — a unified, router-based system",
"significance": "The disillusionment phase of AGI expectations; Altman himself acknowledged that, by the definition of AGI, something important is still missing",
"source_url": "https://openai.com/index/introducing-gpt-5/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0662",
"date": "2025-08-11",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "Nvidia, AMD, US government",
"title": "Nvidia and AMD reportedly agree to pay US 15% of China AI chip revenue",
"event": "Reported agreement to pay the US government 15% of revenue from AI chip sales in China",
"significance": "Export controls in effect turned into a negotiation over tariffs and revenue sharing",
"source_url": "https://www.cnn.com/2025/08/11/china/us-china-trade-nvidia-chips-intl-hnk",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0663",
"date": "2025-08-11",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "",
"actor": "Voyage AI (MongoDB)",
"title": "Voyage AI launches rerank-2.5 and rerank-2.5-lite",
"event": "rerank-2.5 and rerank-2.5-lite launched — reranking that follows natural-language instructions, 32K context",
"significance": "Rerankers change from fixed functions into components steered by instructions. Comparison figures are the vendor's own measurements",
"source_url": "https://blog.voyageai.com/2025/08/11/rerank-2-5/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0664",
"date": "2025-08-13",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "xAI",
"title": "Co-founder Igor Babuschkin exits xAI, founds AI-safety fund Babuschkin Ventures",
"event": "Co-founder Igor Babuschkin left and founded Babuschkin Ventures, which invests in AI safety research",
"significance": "The departure of a key figure who led early engineering",
"source_url": "https://techcrunch.com/2025/08/13/co-founder-of-elon-musks-xai-departs-the-company/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0665",
"date": "2025-08-13",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Geoffrey Hinton",
"title": "Hinton rejects control-centered approach at Ai4 conference",
"event": "Rejected the control-centered approach at the Ai4 conference — arguing that the only model of a more intelligent being controlled by a less intelligent one is a mother controlled by her baby, and that AI should be designed with maternal instincts",
"significance": "A move from 2023's risk warnings to a 2025 alignment prescription. Head-on skepticism toward alignment strategies built on control and obedience",
"source_url": "https://www.entrepreneur.com/business-news/godfather-of-ai-geoffrey-hinton-ai-needs-maternal-instincts/495867",
"source_type": "secondary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0666",
"date": "2025-08-19",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Mustafa Suleyman",
"title": "Mustafa Suleyman publishes Seemingly Conscious AI is coming",
"event": "Published Seemingly Conscious AI is coming — defines SCAI as a system that not only imitates conversation but convinces people it is itself a new kind of conscious person, and concludes that we should build AI for people, not build digital people",
"significance": "Gives the debate over AI consciousness and personhood an industry-side vocabulary, and introduces the frame that claims for AI rights will divide society",
"source_url": "https://mustafa-suleyman.ai/seemingly-conscious-ai-is-coming",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0667",
"date": "2025-08-20",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "xAI",
"title": "xAI publishes its Risk Management Framework",
"event": "xAI Risk Management Framework published — sets numerical thresholds, such as an answer rate below 1 in 20 on restricted biological and chemical threat queries and a MASK dishonesty rate below 1 in 2 for loss of control",
"significance": "Stating quantitative thresholds is unusual, but criteria such as a dishonesty rate under 50% drew criticism from outside safety researchers as far too lax",
"source_url": "https://data.x.ai/2025-08-20-xai-risk-management-framework.pdf",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0668",
"date": "2025-08-21",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "State Council of China",
"title": "China's State Council issues opinions on deepening the AI+ action",
"event": "Opinions on deepening the implementation of the AI+ action issued — sets targets of more than 70% adoption of next-generation intelligent terminals and agent applications by 2027, and more than 90% by 2030. Six key actions",
"significance": "A document in which China made adoption rates, not the model race, its national target metric. Policy language shifts from development to diffusion",
"source_url": "https://www.mee.gov.cn/zcwj/gwywj/202508/t20250827_1126207.shtml",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1248",
"date": "2025-08-21",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "6",
"actor": "DeepSeek",
"title": "DeepSeek releases DeepSeek-V3.1 with hybrid thinking",
"event": "Released DeepSeek-V3.1 — one model switching between Think and Non-Think modes, 671B total / 37B active, 128K context, 840B tokens of long-context continued pretraining on top of V3, MIT",
"significance": "Billed by DeepSeek as its first step toward the agent era; the API's deepseek-chat and deepseek-reasoner became the non-thinking and thinking modes of one model, merging what had been separate V3 and R1 offerings",
"source_url": "https://deepseek.com/en/news/deepseek-v3-1",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E0669",
"date": "2025-08-24",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "xAI",
"title": "xAI releases Grok 2 (2.5) weights on Hugging Face",
"event": "Grok 2 (2.5) weights released on Hugging Face — under xAI's own Grok 2 Community License rather than Apache 2.0",
"significance": "A turning point where the release policy retreated from Apache 2.0 to a custom community license. Because of its non-compete clause, opinions differ on whether it meets the open-source definition",
"source_url": "https://techcrunch.com/2025/08/24/elon-musk-says-xai-has-open-sourced-grok-2-5/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0670",
"date": "2025-08-25",
"date_precision": "day",
"axis": "4",
"axis_name": "MCP & Skills Standardization",
"also_axes": "",
"actor": "Anthropic",
"title": "Anthropic launches Claude for Chrome research preview",
"event": "Claude for Chrome research preview",
"significance": "Safety figures for browser control published — attack success rate in autonomous mode cut from 23.6% to 11.2%, and for browser-specific attacks from 35.7% to 0%",
"source_url": "https://claude.com/blog/claude-for-chrome",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0671",
"date": "2025-08-25",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Nous Research",
"title": "Nous Research releases Hermes 4",
"event": "Hermes 4 released — corpus expanded to about 5 million samples (60B tokens), centered on validated reasoning traces; <think> hybrid reasoning; 14B/70B/405B weights fully released",
"significance": "An approach that benchmarks lower refusal rates as an alignment metric in their own right. The open community formalizes an alternative axis to safety alignment",
"source_url": "https://arxiv.org/abs/2508.18255",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0672",
"date": "2025-08-26",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "",
"actor": "Google",
"title": "Gemini 2.5 Flash Image (nano-banana)",
"event": "Gemini 2.5 Flash Image (nano-banana)",
"significance": "Character-consistent editing reaches mass-market product quality; carries an invisible SynthID watermark",
"source_url": "https://developers.googleblog.com/en/introducing-gemini-2-5-flash-image/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0673",
"date": "2025-08-26",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "UN General Assembly",
"title": "UN General Assembly adopts resolution A/RES/79/325 by consensus",
"event": "Resolution A/RES/79/325 adopted by consensus — establishes the Independent International Scientific Panel on AI and the Global Dialogue on AI Governance",
"significance": "Institutionalizes AI governance at the UN level",
"source_url": "https://www.un.org/independent-international-scientific-panel-ai/en/faq",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0674",
"date": "2025-08-27",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "Anthropic, OpenAI",
"title": "Anthropic and OpenAI publish a mutual alignment evaluation pilot",
"event": "Mutual alignment evaluation pilot published",
"significance": "The first cross-company safety evaluation between competitors",
"source_url": "https://alignment.anthropic.com/2025/openai-findings",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0675",
"date": "2025-08-28",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "xAI",
"title": "xAI releases grok-code-fast-1",
"event": "grok-code-fast-1 released — an agentic coding model; $0.20/M input and $1.50/M output; 70.8% on SWE-Bench-Verified (the announcement states this was measured with xAI's own harness)",
"significance": "A strategy of giving away a low-cost coding model through Copilot, Cursor, Cline and others to win market share first",
"source_url": "https://x.ai/news/grok-code-fast-1",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0676",
"date": "2025-08-28",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "",
"actor": "Orion Weller, Michael Boratko et al. (Google DeepMind, Johns Hopkins)",
"title": "On the Theoretical Limitations of Embedding-Based Retrieval published",
"event": "On the Theoretical Limitations of Embedding-Based Retrieval published — shows in theory and experiments that the embedding dimension limits the number of top-k document subsets that can be returned. On the LIMIT dataset, SOTA models fail even on very simple queries",
"significance": "A rebuttal showing that the limits of single-vector retrieval are fundamental, dimensional limits that more data or bigger models cannot fix. A theoretical basis for a return to multi-vector, sparse and hybrid retrieval",
"source_url": "https://arxiv.org/abs/2508.21038",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0677",
"date": "2025-09-08",
"date_precision": "day",
"axis": "4",
"axis_name": "MCP & Skills Standardization",
"also_axes": "",
"actor": "MCP community",
"title": "MCP Registry preview launched",
"event": "MCP Registry preview launched",
"significance": "Resolves the fragmentation of server discovery, with a structure of a central registry plus private subregistries",
"source_url": "https://blog.modelcontextprotocol.io/posts/2025-09-08-mcp-registry-preview/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0678",
"date": "2025-09-09",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "Mistral AI",
"title": "Mistral AI raises €1.7 billion at €11.7 billion post-money",
"event": "Raised €1.7 billion at a post-money valuation of €11.7 billion. The lead investor is chip-equipment maker ASML",
"significance": "Europe's largest AI funding round. That the lead was not a VC but the sole supplier of EUV equipment reveals its industrial-policy character",
"source_url": "https://mistral.ai/news/mistral-ai-raises-1-7-b-to-accelerate-technological-progress-with-ai/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0679",
"date": "2025-09-10",
"date_precision": "day",
"axis": "19",
"axis_name": "On-Device & Local Inference",
"also_axes": "",
"actor": "Arm",
"title": "Arm announces Lumex CSS platform",
"event": "Lumex CSS platform announced — a C1 CPU cluster with SME2, and the Mali G1-Ultra. KleidiAI integrated into PyTorch ExecuTorch, Google LiteRT, Alibaba MNN and Microsoft ONNX Runtime. Claims up to 5× AI performance, 4.7× lower latency on speech workloads and 2.8× faster audio generation",
"significance": "An approach that tackles the on-device inference bottleneck with a CPU instruction-set extension (SME2) rather than a dedicated NPU. Framework integrations were announced at the same time, so it is deployed through the software path, not through silicon. The figures are the vendor's own claims",
"source_url": "https://newsroom.arm.com/news/announcing-lumex-css-platform-ai-era",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W36"
},
{
"id": "E0680",
"date": "2025-09-11",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "1",
"actor": "Anthropic (Ken Aizawa)",
"title": "Anthropic publishes Writing Effective Tools for Agents",
"event": "Writing Effective Tools for Agents published — choosing which tools to build, namespacing, returning meaningful context, token efficiency, and prompt-engineering tool descriptions",
"significance": "A document showing that prompt engineering has moved into the design of tool definitions",
"source_url": "https://www.anthropic.com/engineering/writing-tools-for-agents",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0681",
"date": "2025-09-13",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "xAI",
"title": "xAI lays off 500 on data annotation team (about a third of 1,500)",
"event": "Laid off 500 people from its data annotation team (about a third of the 1,500 in total). Announced it would instead expand specialist AI tutors 10-fold",
"significance": "Data strategy shifts from generalist human labeling to labeling by experts in STEM, finance, medicine and safety",
"source_url": "https://techcrunch.com/2025/09/13/xai-reportedly-lays-off-500-workers-from-data-annotation-team",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0682",
"date": "2025-09-15",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "Cyberspace Administration of China, National Cybersecurity Standardization Technical Committee",
"title": "China releases AI Safety Governance Framework 2.0",
"event": "AI Safety Governance Framework 2.0 released — refines the risk taxonomy, studies and explores risk grading, and dynamically adjusts prevention and governance measures",
"significance": "The document in which China moved its AI governance from risk classification to risk grading. A sign of convergence with EU-style risk tiers",
"source_url": "https://www.cac.gov.cn/2025-09/15/c_1759653448369123.htm",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0683",
"date": "2025-09-16",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "",
"actor": "Surge, Mercor, Mechanize, Prime Intellect",
"title": "A market for RL environments takes shape",
"event": "A market for RL environments took shape",
"significance": "The bottleneck shifts from static datasets to interactive environments that check outcomes. At the same time, skeptics point to reward hacking and rapid obsolescence",
"source_url": "https://techcrunch.com/2025/09/21/silicon-valley-bets-big-on-environments-to-train-ai-agents/",
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},
{
"id": "E0684",
"date": "2025-09-16",
"date_precision": "day",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
"also_axes": "",
"actor": "FutureHouse",
"title": "Humanity's Last Exam bio/chem error rate put at 29%; HLE's own review finds 18%",
"event": "Raised a 29% error rate in the biology and chemistry questions of Humanity's Last Exam; the HLE team's own review put it at 18%, and it announced rolling revisions",
"significance": "A case that showed, through public refutation and response, that the answer keys of frontier benchmarks can themselves be wrong",
"source_url": "https://www.futurehouse.org/research/hle-exam",
"source_type": "primary",
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},
{
"id": "E0685",
"date": "2025-09-17",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "DeepSeek (深度求索)",
"title": "DeepSeek-R1 paper published in Nature",
"event": "DeepSeek-R1 paper published in Nature — claims that reasoning ability can be induced by pure reinforcement learning alone, without human-labeled reasoning trajectories. Reviewed by eight referees; the review reports and the authors' responses were published alongside it",
"significance": "The first frontier LLM to appear in a journal after formal peer review. A crack in the practice of relying on corporate technical reports",
"source_url": "https://www.nature.com/articles/s41586-025-09422-z",
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},
{
"id": "E0686",
"date": "2025-09-17",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "Chinese regulators",
"title": "China reportedly tells companies to stop buying Nvidia AI chips",
"event": "Reportedly ordered domestic companies to stop buying Nvidia AI chips",
"significance": "The first reversal in the direction of controls — a block on the demand side",
"source_url": "https://www.bloomberg.com/news/articles/2025-09-17/china-tells-companies-to-stop-buying-nvidia-s-repurposed-ai-chip",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0687",
"date": "2025-09-19",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "OpenAI, Apollo Research",
"title": "OpenAI and Apollo Research stress-test anti-scheming training",
"event": "Stress test of anti-scheming training — o3's covert actions fell from 13% to 0.4%",
"significance": "The authors admit they cannot rule out that the reduction was driven by evaluation awareness",
"source_url": "https://arxiv.org/abs/2509.15541",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0688",
"date": "2025-09-19",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "1",
"actor": "xAI",
"title": "xAI releases Grok 4 Fast",
"event": "Grok 4 Fast released — reasoning and non-reasoning modes unified in one set of weights and switched via the system prompt. 2M-token context, 40% fewer thinking tokens",
"significance": "An early case of merging reasoning and non-reasoning models into a single set of weights — a direction that later became the industry standard",
"source_url": "https://x.ai/news/grok-4-fast",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0689",
"date": "2025-09-19",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "US District Court for the Southern District of New York",
"title": "Discovery order in In re OpenAI Copyright Infringement Litigation (25-md-3143)",
"event": "Discovery order in In re OpenAI Copyright Infringement Litigation (25-md-3143)",
"significance": "Confirms that multiple lawsuits have been consolidated and are proceeding as MDL 3143",
"source_url": "https://caselaw.findlaw.com/court/us-dis-crt-sd-new-yor/117775559.html",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0690",
"date": "2025-09-19",
"date_precision": "day",
"axis": "19",
"axis_name": "On-Device & Local Inference",
"also_axes": "",
"actor": "Ollama",
"title": "Ollama previews cloud models",
"event": "Cloud models preview — keeps the local tool chain while offloading only large models to data-center hardware",
"significance": "A local deployment tool expands into hybrid execution. The point at which the definition of running locally began to blur",
"source_url": "https://ollama.com/blog",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0691",
"date": "2025-09-21",
"date_precision": "day",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
"also_axes": "",
"actor": "Scale AI",
"title": "Scale AI releases SWE-Bench Pro",
"event": "SWE-Bench Pro released — 41 repositories and 1,865 tasks, in three tiers: public, held-out and commercial",
"significance": "A follow-up design that responds to contamination and saturation in the SWE-bench family. An attempt to prevent leakage structurally by separating tiers",
"source_url": "https://arxiv.org/abs/2509.16941",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0692",
"date": "2025-09-22",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "Google DeepMind",
"title": "Frontier Safety Framework v3.0",
"event": "Frontier Safety Framework v3.0",
"significance": "Adds harmful manipulation as a new CCL and tackles misalignment, such as resistance to shutdown, head-on",
"source_url": "https://deepmind.google/blog/strengthening-our-frontier-safety-framework/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0693",
"date": "2025-09-22",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "OpenAI, NVIDIA",
"title": "OpenAI and NVIDIA partner on at least 10 GW; NVIDIA to invest up to $100 billion",
"event": "Partnership to deploy at least 10 GW; NVIDIA to invest up to $100 billion in stages",
"significance": "The starting point of the circular-financing debate",
"source_url": "https://openai.com/index/openai-nvidia-systems-partnership/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0694",
"date": "2025-09-22",
"date_precision": "day",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
"also_axes": "",
"actor": "Meta",
"title": "Meta releases GAIA2 and the open-source execution framework ARE",
"event": "GAIA2 and the open-source execution framework ARE released",
"significance": "A shift from evaluating static information retrieval to measuring agent robustness in dynamic, asynchronous environments",
"source_url": "https://huggingface.co/blog/gaia2",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0695",
"date": "2025-09-24",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "Cohere",
"title": "Cohere raises an extra $100 million at a $7 billion valuation",
"event": "Raised an additional $100 million at a $7 billion valuation. AMD Ventures and NVIDIA both took part",
"significance": "A rare case of AMD and NVIDIA coming in as investors together. Sidesteps the frontier race by positioning itself on enterprise and sovereign AI",
"source_url": "https://betakit.com/coheres-valuation-hits-7-billion-usd-following-100-million-round-extension/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0696",
"date": "2025-09-25",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "Google DeepMind",
"title": "Gemini Robotics 1.5 and ER 1.5",
"event": "Gemini Robotics 1.5 and ER 1.5 — think-before-act, transfer across different robot embodiments",
"significance": "Establishes a two-track release strategy: only ER is opened via API, while the VLA model itself stays closed",
"source_url": "https://deepmind.google/blog/gemini-robotics-15-brings-ai-agents-into-the-physical-world/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0697",
"date": "2025-09-25",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "US District Court for the Northern District of California",
"title": "Preliminary approval of the $1.5 billion Bartz v. Anthropic settlement",
"event": "Preliminary approval of the $1.5 billion Bartz v. Anthropic settlement — preliminary, not final",
"significance": "The largest settlement in an AI training-data dispute",
"source_url": "https://authorsguild.org/advocacy/artificial-intelligence/what-authors-need-to-know-about-the-anthropic-settlement/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0698",
"date": "2025-09-25",
"date_precision": "day",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
"also_axes": "",
"actor": "OpenAI",
"title": "OpenAI releases GDPval",
"event": "GDPval released — experts grade 1,320 real work products from 44 occupations across 9 industries",
"significance": "A design that moves the object of evaluation away from answer-key grading to real work of economic value",
"source_url": "https://openai.com/index/gdpval/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0699",
"date": "2025-09-26",
"date_precision": "day",
"axis": "4",
"axis_name": "MCP & Skills Standardization",
"also_axes": "",
"actor": "Cloudflare",
"title": "Cloudflare announces Code Mode",
"event": "Code Mode announced",
"significance": "A proposal to convert MCP tools into a TypeScript API and have the LLM write code; the gain is that intermediate results never pass through the context",
"source_url": "https://blog.cloudflare.com/code-mode/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0700",
"date": "2025-09-26",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Richard Sutton",
"title": "Sutton rejects the LLM path outright on Dwarkesh Podcast",
"event": "Flatly rejected the LLM path on Dwarkesh Podcast — arguing that large language models imitate people rather than figure out what to do, and that because they make no predictions about what will happen next, they have no ground truth",
"significance": "The author of the Bitter Lesson himself declared that LLMs do not fit the Bitter Lesson. Ended a situation in which both camps cited the same text as support",
"source_url": "https://www.dwarkesh.com/p/richard-sutton",
"source_type": "secondary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0701",
"date": "2025-09-29",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "Anthropic",
"title": "Context editing and memory tool launch in beta on the Claude API",
"event": "Context editing and a memory tool launched in beta on the Claude API — 84% less token consumption in a 100-turn web search evaluation",
"significance": "Context management becomes a built-in API feature",
"source_url": "https://www.anthropic.com/news/context-management",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0702",
"date": "2025-09-29",
"date_precision": "day",
"axis": "5",
"axis_name": "Loop & Harness Engineering",
"also_axes": "2",
"actor": "Anthropic",
"title": "Effective context engineering for AI agents",
"event": "Effective context engineering for AI agents",
"significance": "Names four patterns: compaction, structured note-taking, sub-agents and just-in-time retrieval",
"source_url": "https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0703",
"date": "2025-09-29",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "",
"actor": "DeepSeek",
"title": "DeepSeek-V3.2-Exp",
"event": "DeepSeek-V3.2-Exp — the first to use DeepSeek Sparse Attention",
"significance": "Puts fine-grained sparse attention into a commercial API and cuts long-context costs",
"source_url": "https://api-docs.deepseek.com/news/news250929",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0704",
"date": "2025-09-29",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "",
"actor": "Psyche Team",
"title": "Psyche shifts from proof to performance",
"event": "Psyche shifted from proof to performance — a new trainer abstraction adds support for SFT and RL; training of Hermes 4 on ByteDance Seed-OSS-36B began on the Psyche network",
"significance": "Distributed training moves beyond pretraining demos into the post-training path of real product models",
"source_url": "https://nousresearch.com/the-next-phase-of-psyche/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0705",
"date": "2025-09-29",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "Governor of California",
"title": "California governor signs SB 53 (TFAIA), the frontier AI transparency law",
"event": "SB 53 (Transparency in Frontier Artificial Intelligence Act, TFAIA) signed — date of signing. Framework disclosure, reporting of critical safety incidents, whistleblower protections, civil penalties enforced by the AG, and the creation of CalCompute",
"significance": "Passed one year after the SB 1047 veto, scaled back to focus on transparency. It went on to compel each lab to publish its framework",
"source_url": "https://www.gov.ca.gov/2025/09/29/governor-newsom-signs-sb-53-advancing-californias-world-leading-artificial-intelligence-industry/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0706",
"date": "2025-09-29",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "",
"actor": "NVIDIA (Felix Abecassis et al.)",
"title": "Pretraining Large Language Models with NVFP4 published",
"event": "Pretraining Large Language Models with NVFP4 published — a 12B model trained on 10T tokens (the longest 4-bit training run in public documents), matching the FP8 baseline in training loss and accuracy",
"significance": "The strongest positive evidence that FP4 training actually works. However, it is the vendor's own report, and no independent reproduction has been confirmed",
"source_url": "https://arxiv.org/abs/2509.25149",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1249",
"date": "2025-09-29",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "3",
"actor": "Anthropic",
"title": "Anthropic releases Claude Sonnet 4.5",
"event": "Claude Sonnet 4.5 released — OSWorld 61.4% and SWE-bench Verified 77.2% (company-reported), with pricing unchanged at $3/$15 per 1M tokens",
"significance": "Anthropic said it observed the model maintaining focus for more than 30 hours on complex multi-step tasks; released under ASL-3 protections together with the Claude Agent SDK",
"source_url": "https://www.anthropic.com/news/claude-sonnet-4-5",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E0707",
"date": "2025-09-30",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "",
"actor": "OpenAI",
"title": "OpenAI launches Sora 2 and a social app",
"event": "Sora 2 and a social app",
"significance": "Synchronized dialogue and sound effects, better physical accuracy, and a feature for inserting yourself (cameo)",
"source_url": "https://openai.com/index/sora-2/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0708",
"date": "2025-09-30",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "BIS",
"title": "Affiliates Rule",
"event": "Affiliates Rule — entities owned 50% or more by Entity List companies are automatically subject to controls",
"significance": "Controls automatically extend to thousands of entities",
"source_url": "https://www.federalregister.gov/documents/2025/09/30/2025-19001/expansion-of-end-user-controls-to-cover-affiliates-of-certain-listed-entities",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0709",
"date": "2025-10",
"date_precision": "month",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "",
"actor": "Salesforce AI Research, CMU",
"title": "Webscale-RL",
"event": "Webscale-RL — automatically converts pretraining documents into verifiable QA pairs",
"significance": "Matches continual pretraining with up to 100 times fewer tokens — a direct technical answer to data exhaustion",
"source_url": "https://arxiv.org/abs/2510.06499",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0710",
"date": "2025-10-01",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "Minki Kang et al.",
"title": "ACON published",
"event": "ACON published — iterative compression guided by failure analysis cuts the tokens in long-horizon agents' observation and action histories by 26–54%",
"significance": "A direction that shrinks context without fine-tuning, so that even small models can be used as long-horizon agents",
"source_url": "https://arxiv.org/abs/2510.00615",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0711",
"date": "2025-10-02",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "Semiconductor Engineering",
"title": "Semiconductor Engineering article on AI in packaging and assembly",
"event": "Article on the state of AI in packaging and assembly — cites Tignis (Cohu), PDF Solutions, Microtronic and Synopsys, but the only quantitative figures come from one cited case study, and none of the companies cited confirmed figures of their own or use in volume production",
"significance": "A snapshot of how far AI in back-end semiconductor processes has actually come. Predictive maintenance, anomaly detection and transfer learning are mentioned, but there are no published figures from volume production",
"source_url": "https://semiengineering.com/smarter-packaging-how-ai-is-reshaping-assembly-and-materials-control/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0712",
"date": "2025-10-03",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "",
"actor": "OpenAI (Sam Altman)",
"title": "Sora 2 swaps copyright opt-out for rightsholder opt-in with granular controls",
"event": "Sora 2's copyright policy switched from opt-out to opt-in by rightsholders, with granular controls",
"significance": "How rights are handled in generated video becomes a live industry issue",
"source_url": "https://blog.samaltman.com/sora-update-number-1",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0713",
"date": "2025-10-06",
"date_precision": "day",
"axis": "4",
"axis_name": "MCP & Skills Standardization",
"also_axes": "",
"actor": "OpenAI",
"title": "Apps SDK",
"event": "Apps SDK — an MCP-based platform for ChatGPT apps",
"significance": "MCP goes beyond backend connections to become a distribution layer for consumer apps",
"source_url": "https://openai.com/index/introducing-apps-in-chatgpt/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0714",
"date": "2025-10-06",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "Anthropic",
"title": "Petri",
"event": "Petri — an open-source automated auditing tool",
"significance": "Safety evaluation moves from manual red-teaming to agent pipelines",
"source_url": "https://www.anthropic.com/research/petri-open-source-auditing",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0715",
"date": "2025-10-06",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "OpenAI, AMD",
"title": "6 GW supply deal gives OpenAI warrants for up to 160 million AMD shares",
"event": "6 GW of supply, plus warrants for up to 160 million AMD shares",
"significance": "Institutionalizes a structure in which the customer receives equity in the supplier",
"source_url": "https://openai.com/index/openai-amd-strategic-partnership/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0716",
"date": "2025-10-07",
"date_precision": "day",
"axis": "4",
"axis_name": "MCP & Skills Standardization",
"also_axes": "",
"actor": "Google",
"title": "Gemini 2.5 Computer Use",
"event": "Gemini 2.5 Computer Use",
"significance": "Responds with a dedicated, browser-specialized model; states explicitly that it is not yet optimized for desktop OS control",
"source_url": "https://blog.google/innovation-and-ai/models-and-research/google-deepmind/gemini-computer-use-model/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0717",
"date": "2025-10-09",
"date_precision": "day",
"axis": "4",
"axis_name": "MCP & Skills Standardization",
"also_axes": "",
"actor": "Anthropic",
"title": "Claude Code plugins enter public beta",
"event": "Claude Code plugins in public beta",
"significance": "A distribution unit that bundles slash commands, subagents, MCP servers and hooks into a single git repository",
"source_url": "https://claude.com/blog/claude-code-plugins",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0718",
"date": "2025-10-09",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "Figure",
"title": "Figure unveils Figure 03",
"event": "Figure 03 unveiled — fingertips that sense pressure as small as 3 grams, palm cameras",
"significance": "Production capacity targets for BotQ were set out alongside. All unit numbers are vendor targets",
"source_url": "https://www.figure.ai/news/introducing-figure-03",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0719",
"date": "2025-10-13",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "",
"actor": "NVIDIA",
"title": "NVIDIA announces DGX Spark (ships Oct 15)",
"event": "DGX Spark announced (shipping Oct 15) — 128 GB of unified memory",
"significance": "Inference on 200B models and fine-tuning of 70B models on a desktop; local machines become agent runtimes",
"source_url": "https://nvidianews.nvidia.com/news/nvidia-dgx-spark-arrives-for-worlds-ai-developers",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0720",
"date": "2025-10-13",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "OpenAI, Broadcom",
"title": "OpenAI and Broadcom: 10 GW of OpenAI-designed accelerators (H2 2026–2029)",
"event": "10 GW of OpenAI-designed accelerators (second half of 2026 through 2029)",
"significance": "Vertical integration at a frontier lab",
"source_url": "https://openai.com/index/openai-and-broadcom-announce-strategic-collaboration/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0721",
"date": "2025-10-13",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Andrej Karpathy",
"title": "Karpathy releases nanochat",
"event": "nanochat released — billed as the best ChatGPT that $100 can buy, it reproduces the full pipeline, from the tokenizer through pretraining, midtraining, SFT and RL to a web UI, on a single 8xH100 node for about $92",
"significance": "Brought frontier-model training down from a secret to a reproducible recipe. The culmination of the nanoGPT and llm.c lineage",
"source_url": "https://github.com/karpathy/nanochat/discussions/1",
"source_type": "primary",
"claim_type": "community",
"added_week": "2026-W35"
},
{
"id": "E0722",
"date": "2025-10-15",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "",
"actor": "PyTorch Foundation",
"title": "PyTorch 2.9 released",
"event": "PyTorch 2.9 released — a wider stable libtorch ABI for third-party C++/CUDA extensions, symmetric memory for writing multi-GPU kernels, an error/resume toggle for torch.compile graph breaks, wheel variants for ROCm, XPU and CUDA 13, and FlexAttention enabled on Intel GPUs. 452 contributors and 3,216 commits since 2.8",
"significance": "A direction that lets users write multi-GPU kernels directly at the framework API level. On top of NCCL-style collective communication, user kernels gain one more path",
"source_url": "https://pytorch.org/blog/pytorch-2-9/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W36"
},
{
"id": "E1250",
"date": "2025-10-15",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "13",
"actor": "Anthropic",
"title": "Anthropic releases Claude Haiku 4.5",
"event": "Claude Haiku 4.5 released — a small model at $1/$5 per 1M tokens, 73.3% on SWE-bench Verified (company-reported)",
"significance": "Anthropic said it gives coding performance similar to Sonnet 4, state of the art five months earlier, at one-third the cost and more than twice the speed; released under ASL-2 after testing found only limited CBRN risk",
"source_url": "https://www.anthropic.com/news/claude-haiku-4-5",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E0723",
"date": "2025-10-16",
"date_precision": "day",
"axis": "4",
"axis_name": "MCP & Skills Standardization",
"also_axes": "",
"actor": "Anthropic",
"title": "Anthropic launches Agent Skills (SKILL.md, progressive disclosure)",
"event": "Agent Skills launched (SKILL.md, progressive disclosure)",
"significance": "Packages procedural knowledge as folders and loads it only when needed, in three stages: metadata, body, bundled files",
"source_url": "https://claude.com/blog/skills",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0724",
"date": "2025-10-17",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Andrej Karpathy",
"title": "Karpathy appears on Dwarkesh Podcast",
"event": "Appeared on Dwarkesh Podcast — answered that AGI is 10 years away, and, in reaction to the industry's year-of-agents rhetoric, said we will be working with agents for 10 years. He also said we are building ghosts, not animals, likening pretraining to a crude evolution",
"significance": "A rare case of a lab veteran publicly pushing timelines back. The phrase decade of agents became the standard quote for pushing back on the hype",
"source_url": "https://www.dwarkesh.com/p/andrej-karpathy",
"source_type": "secondary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0725",
"date": "2025-10-17",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Andrej Karpathy",
"title": "In the same interview, Karpathy says nanochat was not built by vibe coding",
"event": "In the same interview, stated that nanochat was not built with vibe coding — saying coding models kept misunderstanding his custom implementation and suggesting deprecated APIs, and that he mostly uses autocomplete and limits vibe coding to boilerplate or unfamiliar languages",
"significance": "The coiner of the term publicly narrowed its scope. His own rebuttal to stretching vibe coding to cover production development",
"source_url": "https://www.dwarkesh.com/p/andrej-karpathy",
"source_type": "secondary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0726",
"date": "2025-10-19",
"date_precision": "day",
"axis": "19",
"axis_name": "On-Device & Local Inference",
"also_axes": "",
"actor": "llama.cpp community",
"title": "NVFP4 support requested in llama.cpp Discussion #16668",
"event": "NVFP4 support requested in Discussion #16668. A maintainer pointed to the MXFP4 support PR as the template",
"significance": "The point at which the path was set for GGUF to absorb vendor FP4 formats one after another",
"source_url": "https://github.com/ggml-org/llama.cpp/discussions/16668",
"source_type": "primary",
"claim_type": "community",
"added_week": "2026-W35"
},
{
"id": "E0727",
"date": "2025-10-20",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "",
"actor": "OpenAI, SAG-AFTRA",
"title": "Sora 2 tightens deepfake guardrails on likeness rights",
"event": "Sora 2's deepfake guardrails on likeness rights strengthened",
"significance": "A likeness-rights track takes shape, separate from the copyright track",
"source_url": "https://www.cnbc.com/2025/10/20/open-ai-sora-bryan-cranston-sag-aftra.html",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0728",
"date": "2025-10-21",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "Hendrycks, Bengio and 29 others",
"title": "A Definition of AGI",
"event": "A Definition of AGI — 10 cognitive domains based on CHC theory; GPT-4 scores 27%, GPT-5 57%",
"significance": "Moves the definition of AGI from rhetoric to a psychometric scale; the biggest deficit is long-term memory storage",
"source_url": "https://arxiv.org/abs/2510.18212",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0729",
"date": "2025-10-21",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "2",
"actor": "DeepSeek",
"title": "DeepSeek-OCR",
"event": "DeepSeek-OCR — Contexts Optical Compression",
"significance": "97% OCR precision at compression below 10×, about 60% at 20×. A shift in thinking: images as a medium for compressing context",
"source_url": "https://arxiv.org/abs/2510.18234",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0730",
"date": "2025-10-22",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "",
"actor": "LangChain",
"title": "LangChain 1.0 and LangGraph 1.0 released",
"event": "LangChain 1.0 and LangGraph 1.0 released — create_agent as the standard loop, middleware that hooks into the agent loop, durable state",
"significance": "Formalizes customization of the agent loop as the concept of middleware",
"source_url": "https://www.langchain.com/blog/langchain-langgraph-1dot0",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0731",
"date": "2025-10-22",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "",
"actor": "PyTorch (Meta)",
"title": "Helion released in beta",
"event": "Helion beta released — compiles a Python-embedded DSL into autotuned Triton kernels. An attention kernel takes 30 lines in Helion versus 120 in Triton; geometric-mean speedup over eager of 3.27× on B200 (torch.compile 2.7×, hand-written Triton 1.76×), and 2.37× on MI350X (MI350 series)",
"significance": "Just as Triton abstracted CUDA by one level, Helion abstracts Triton by one more. The next layer in the lineage of kernel authoring",
"source_url": "https://pytorch.org/blog/helion/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W36"
},
{
"id": "E0732",
"date": "2025-10-22",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "",
"actor": "PyTorch (Meta)",
"title": "PyTorch releases Monarch",
"event": "Monarch released — a distributed framework that uses a single controller instead of SPMD to treat a cluster like an array. Separates the control plane (messaging) from the data plane (RDMA). In the VERL integration it scaled from 16 to 2,048 H200 GPUs; with the torchft integration, injecting a failure every 3 minutes on 240 H100 GPUs, recovery was 60% faster than a full SLURM restart (90 seconds on average for process failures, 2.5 minutes for machine failures)",
"significance": "The point at which the single-controller model proposed by Google's Pathways in 2022 entered the core PyTorch lineage. TorchTitan becomes an actor inside Monarch, and torchforge is built on top of Monarch",
"source_url": "https://pytorch.org/blog/introducing-pytorch-monarch/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W36"
},
{
"id": "E0733",
"date": "2025-10-23",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "Anthropic, Google",
"title": "Anthropic to use up to 1 million Google TPUs, with 1 GW+ online in 2026",
"event": "Up to 1 million TPUs, with 1 GW+ coming online in 2026",
"significance": "The first mega-deal for alternative silicon outside NVIDIA",
"source_url": "https://www.googlecloudpresscorner.com/2025-10-23-Anthropic-to-Expand-Use-of-Google-Cloud-TPUs-and-Services",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0734",
"date": "2025-10-27",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "xAI",
"title": "xAI launches Grokipedia v0.1 (800,000+ articles)",
"event": "Grokipedia v0.1 launched (800,000+ articles). 5.6 million articles by early 2026",
"significance": "The Guardian pointed to the spread of white-nationalist talking points, and PolitiFact to missing sources and misquotation. A case that exposed the problem of checking AI-generated knowledge bases",
"source_url": "https://en.wikipedia.org/wiki/Grokipedia",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0735",
"date": "2025-10-27",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "",
"actor": "S.D.N.Y. (Stein)",
"title": "In re OpenAI Copyright Infringement Litigation (MDL 25-md-3143) consolidated",
"event": "In re OpenAI Copyright Infringement Litigation (MDL 25-md-3143) consolidated",
"significance": "Seven cases, including those brought by the NYT and the Authors Guild, combined into one MDL",
"source_url": "https://www.courthousenews.com/wp-content/uploads/2025/10/open-ai-motion-to-dismiss-infringement-opinion.pdf",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0736",
"date": "2025-10-28",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "OpenAI, Microsoft",
"title": "OpenAI restructures its governance",
"event": "Governance restructured — OpenAI Group PBC; Microsoft's stake about 27%; a commitment to buy $250 billion of Azure",
"significance": "Moves any declaration of AGI to confirmation by an independent expert panel, and lifts constraints on raising capital",
"source_url": "https://openai.com/index/next-chapter-of-microsoft-openai-partnership/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0737",
"date": "2025-10-28",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "",
"actor": "Amazon",
"title": "Amazon makes Nova Multimodal Embeddings generally available on Bedrock",
"event": "Nova Multimodal Embeddings generally available on Bedrock — text, images, video and audio in a single embedding space",
"significance": "A cloud provider makes unified multimodal embeddings a standard product",
"source_url": "https://aws.amazon.com/about-aws/whats-new/2025/10/amazon-nova-multimodal-embeddings",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0738",
"date": "2025-10-29",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "Anthropic (Jack Lindsey)",
"title": "Emergent Introspective Awareness",
"event": "Emergent Introspective Awareness",
"significance": "Presents causal evidence of model introspection by injecting activations",
"source_url": "https://transformer-circuits.pub/2025/introspection/index.html",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0739",
"date": "2025-10-30",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Jianli Zhao et al. (Renmin University, Oxford, Stanford, Anthropic and others)",
"title": "Chain-of-Thought Hijacking published",
"event": "Chain-of-Thought Hijacking published — bypasses safety refusals by prepending long, harmless reasoning",
"significance": "A counterexample from the attack side to optimism about CoT monitoring",
"source_url": "https://arxiv.org/abs/2510.26418",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0740",
"date": "2025-10-30",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "Scale AI, CAIS",
"title": "Remote Labor Index",
"event": "Remote Labor Index — the best-performing agent automates 2.5% of the work",
"significance": "Quantitative evidence of a failure to transfer to real-world knowledge work",
"source_url": "https://arxiv.org/abs/2510.26787",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0741",
"date": "2025-10-30",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "",
"actor": "Moonshot AI",
"title": "Kimi Linear",
"event": "Kimi Linear — a layer-wise hybrid of KDA and MLA",
"significance": "The first to beat full attention in a fair comparison; up to 75% less KV cache and up to 6× faster decoding at 1M context",
"source_url": "https://arxiv.org/abs/2510.26692",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0742",
"date": "2025-11",
"date_precision": "month",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "",
"actor": "Peter Steinberger",
"title": "Peter Steinberger releases Warelay, a personal AI assistant",
"event": "Released Warelay, a personal AI assistant, which later led to the Clawd → Clawdbot lineage",
"significance": "The starting point that popularized the form of a locally resident agent that uses messaging apps as its interface",
"source_url": "https://en.wikipedia.org/wiki/OpenClaw",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0743",
"date": "2025-11",
"date_precision": "month",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
"also_axes": "",
"actor": "Andon Labs",
"title": "Andon Labs releases Vending-Bench 2 and the multi-agent Vending-Bench Arena",
"event": "Released Vending-Bench 2 and Vending-Bench Arena, a competitive multi-agent version",
"significance": "A long-term consistency evaluation that has a model run a vending machine business for 1 year. Measures the coherence that one-off tasks cannot capture",
"source_url": "https://andonlabs.com/evals/vending-bench-2",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0744",
"date": "2025-11-04",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "2",
"actor": "Anthropic",
"title": "Anthropic publishes Code Execution with MCP",
"event": "Published Code Execution with MCP — exposes MCP servers as code APIs so only the needed tool definitions are loaded. In the example, 150,000 → 2,000 tokens",
"significance": "A structural fix for tool definitions eating up the context",
"source_url": "https://www.anthropic.com/engineering/code-execution-with-mcp",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0745",
"date": "2025-11-04",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "High Court of England and Wales (Justice Joanna Smith)",
"title": "Getty Images v Stability AI [2025] EWHC 2863 (Ch): judgment on the merits",
"event": "Judgment on the merits in Getty Images v Stability AI [2025] EWHC 2863 (Ch) — Getty withdrew its copyright claims over training and outputs during trial (training took place outside UK jurisdiction). The secondary infringement claim was dismissed (the model weights do not contain copies of the original works); only trademark infringement was found, limited to iStock watermarks",
"significance": "Summing it up as simply a loss for Getty is wrong. The core issues were not decided; they were withdrawn",
"source_url": "https://ipkitten.blogspot.com/2025/11/getty-images-v-stability-long-awaited.html",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0746",
"date": "2025-11-06",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "",
"actor": "Google Cloud",
"title": "Google Cloud announces Ironwood (TPU v7)",
"event": "Announced Ironwood (TPU v7) — 9,216 chips per superpod",
"significance": "A TPU for the inference era. Actual GA came on Mar 31, 2026",
"source_url": "https://cloud.google.com/blog/products/compute/ironwood-tpus-and-new-axion-based-vms-for-your-ai-workloads/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1251",
"date": "2025-11-06",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "3;6",
"actor": "Moonshot AI",
"title": "Moonshot AI releases Kimi K2 Thinking",
"event": "Released Kimi K2 Thinking — 1T total / 32B active MoE, 256K context, native INT4 inference via quantization-aware training in post-training, under a Modified MIT License",
"significance": "An open-weight reasoning agent trained to interleave thinking with tool calls; Moonshot claims it sustains 200–300 sequential tool calls without human intervention",
"source_url": "https://huggingface.co/moonshotai/Kimi-K2-Thinking",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E0747",
"date": "2025-11-07",
"date_precision": "day",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
"also_axes": "",
"actor": "Terminal-Bench team",
"title": "Terminal-Bench team releases Terminal-Bench 2.0 and Harbor evaluation harness",
"event": "Released Terminal-Bench 2.0 and Harbor, an evaluation harness",
"significance": "Evaluation infrastructure that adds cloud container execution and an RL/SFT rollout interface to agent CLI evaluation",
"source_url": "https://www.tbench.ai/news/announcement-2-0",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0748",
"date": "2025-11-08",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "",
"actor": "Google Cloud",
"title": "Google Cloud adds Auto Vector Embeddings and Auto Vector Index to AlloyDB AI",
"event": "Added Auto Vector Embeddings and Auto Vector Index (ScaNN AUTO) in preview to AlloyDB AI",
"significance": "An attempt to absorb embedding generation and ANN tuning into the database and eliminate separate pipelines",
"source_url": "https://cloud.google.com/blog/products/databases/alloydb-ai-auto-vector-embeddings-and-auto-vector-index",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0749",
"date": "2025-11-11",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "",
"actor": "LG München I (Munich Regional Court I)",
"title": "Munich court rules in GEMA v. OpenAI",
"event": "First-instance judgment in GEMA v. OpenAI",
"significance": "The first case in Europe to treat memorization as reproduction and to rule out the TDM exception",
"source_url": "https://www.euipo.europa.eu/en/law/recent-case-law/the-higher-regional-court-of-munich-considered-memorization-and-temporary-copies-occurred-in-model-training-as-infringing-reproductions-of-works",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0750",
"date": "2025-11-11",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "",
"actor": "NVIDIA",
"title": "NVIDIA releases NCCL 2.28",
"event": "Released NCCL 2.28 — a Device API that lets kernels initiate communication directly, GPU-Initiated Networking, Copy Engine collectives that use no SMs, and symmetric memory support",
"significance": "A release that supports fusing communication and compute at the kernel level, changing how the communication bottleneck in large-scale distributed training and inference is tackled",
"source_url": "https://developer.nvidia.com/blog/fusing-communication-and-compute-with-new-device-api-and-copy-engine-collectives-in-nvidia-nccl-2-28/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
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{
"id": "E0751",
"date": "2025-11-11",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Yang Zhilin (杨植麟) and others, Moonshot AI",
"title": "Moonshot AI's Yang Zhilin explains the open-source choice in a K2 Thinking AMA",
"event": "In an AMA right after releasing K2 Thinking, said the reason for choosing open source is that AGI should be a pursuit that brings unity rather than division. Denied the widely cited $4.6 million training cost, saying it is not an official figure and that training cost is hard to quantify because research and experiments make up a large share",
"significance": "A statement in which a Chinese lab justified open weights in normative language, and a record of a party directly denying the low-cost narrative",
"source_url": "https://news.qq.com/rain/a/20251111A05PWV00",
"source_type": "secondary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0752",
"date": "2025-11-12",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "",
"actor": "World Labs",
"title": "World Labs launches Marble",
"event": "Launched Marble to the public",
"significance": "Generates editable 3D worlds from text, images and video, and exports them as Gaussian splats and meshes",
"source_url": "https://www.worldlabs.ai/blog/marble-world-model",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0753",
"date": "2025-11-12",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "Anthropic",
"title": "Anthropic announces $50 billion in US data centers (partner: Fluidstack)",
"event": "Announced $50 billion in US data centers (partner: Fluidstack)",
"significance": "Direct infrastructure investment by a lab",
"source_url": "https://www.anthropic.com/news/anthropic-invests-50-billion-in-american-ai-infrastructure",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0754",
"date": "2025-11-12",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "BIS",
"title": "BIS suspends the Affiliates Rule for 1 year, until Nov 9, 2026",
"event": "Suspended the Affiliates Rule for 1 year, until Nov 9, 2026",
"significance": "The US-China truce reflected in regulation. It automatically comes back into force on Nov 10, 2026",
"source_url": "https://www.federalregister.gov/documents/2025/11/12/2025-19846/one-year-suspension-of-expansion-of-end-user-controls-for-affiliates-of-certain-listed-entities",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1252",
"date": "2025-11-12",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "OpenAI",
"title": "OpenAI releases GPT-5.1",
"event": "GPT-5.1 released — the GPT-5 series updated with two models, GPT-5.1 Instant and GPT-5.1 Thinking",
"significance": "OpenAI said Thinking adapts its thinking time more dynamically, roughly twice as fast on the fastest tasks and twice as slow on the slowest, and that the name reflects an iterative upgrade within the GPT-5 generation",
"source_url": "https://openai.com/index/gpt-5-1/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E0755",
"date": "2025-11-17",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "xAI",
"title": "xAI releases Grok 4.1",
"event": "Released Grok 4.1 — the Thinking version ranked first on the LMArena text leaderboard at 1483 Elo. Applied large-scale RL using frontier reasoning models as reward evaluators",
"significance": "A release that made emotional and creative interaction an explicit optimization target",
"source_url": "https://x.ai/news/grok-4-1",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0756",
"date": "2025-11-17",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "Sakana AI",
"title": "Sakana AI raises ¥32 billion in Series B at about ¥432 billion post-money",
"event": "Raised ¥32 billion in a Series B at a post-money valuation of about ¥432 billion. Participants included MUFG, Khosla Ventures, Google and Salesforce Ventures",
"significance": "The largest round for a Japanese AI startup. Its self-definition — avoiding the compute race and taking the efficiency route — was put in writing at the same time",
"source_url": "https://sakana.ai/series-b/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0757",
"date": "2025-11-17",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "Physical Intelligence",
"title": "Physical Intelligence presents π*0.6 and RECAP",
"event": "π*0.6 and RECAP — made espresso all day and assembled 59 boxes at a real chocolate factory",
"significance": "Moved beyond imitation learning to experience-based RL, with over 90% success on the hardest stages",
"source_url": "https://www.pi.website/blog/pistar06",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0758",
"date": "2025-11-18",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "Google DeepMind",
"title": "Gemini 3 Pro / Deep Think",
"event": "Gemini 3 Pro / Deep Think — Deep Think scored 45.1% on ARC-AGI-2",
"significance": "The ARC-AGI-2 SOTA doubled in a single leap",
"source_url": "https://blog.google/products-and-platforms/products/gemini/gemini-3/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0759",
"date": "2025-11-18",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "",
"actor": "Google",
"title": "Gemini 3 Pro",
"event": "Gemini 3 Pro — MMMU-Pro 81%, Video-MMMU 87.6%",
"significance": "A natively multimodal design paid off as an edge in video and document understanding",
"source_url": "https://blog.google/products/gemini/gemini-3/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0760",
"date": "2025-11-18",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "Microsoft, NVIDIA, Anthropic",
"title": "Anthropic's $30B Azure deal; NVIDIA to invest up to $10B, Microsoft up to $5B",
"event": "Anthropic made a $30 billion Azure purchase commitment; NVIDIA will invest up to $10 billion and Microsoft up to $5 billion",
"significance": "The finished form of a three-way structure of cross-investment and purchasing",
"source_url": "https://www.anthropic.com/news/microsoft-nvidia-anthropic-announce-strategic-partnerships",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0761",
"date": "2025-11-19",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "Yann LeCun",
"title": "Yann LeCun resigns as Meta's chief AI scientist to found a world models startup",
"event": "Resigned as Meta's chief AI scientist and founded a startup centered on world models",
"significance": "The biggest skeptic camp splitting off organizationally",
"source_url": "https://www.cnbc.com/2025/11/19/meta-chief-ai-scientist-yann-lecun-is-leaving-the-company-.html",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0762",
"date": "2025-11-19",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "US Department of Commerce",
"title": "US approves up to 35,000 GB300-equivalent chips each for UAE G42, Saudi Humain",
"event": "Approved exports of up to 35,000 GB300-equivalent chips each to the UAE's G42 and Saudi Arabia's Humain",
"significance": "Gulf capital was brought in as a third pole of the compute supply chain",
"source_url": "https://www.commerce.gov/news/press-releases/2025/11/statement-uae-and-saudi-chip-exports",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0763",
"date": "2025-11-19",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "xAI",
"title": "xAI partners with HUMAIN, part of Saudi Arabia's PIF",
"event": "Partnered with HUMAIN, part of Saudi Arabia's PIF — designing, building and operating hyperscale GPU data centers in Saudi Arabia and deploying Grok nationwide",
"significance": "The race to secure compute through Gulf capital. The announcement gives no MW capacity, GPU count or construction start date, so it is all at the planning stage",
"source_url": "https://x.ai/news/grok-goes-global",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0764",
"date": "2025-11-19",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "Figure (BMW site)",
"title": "Figure reports 11 months of results at BMW's Spartanburg plant",
"event": "Spartanburg 11-month results — more than 1,250 hours, more than 90,000 parts, contributed to more than 30,000 X3s",
"significance": "The first quantitative production metrics for a humanoid. However, they were announced by Figure, not documented independently by BMW",
"source_url": "https://www.figure.ai/news/production-at-bmw",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0765",
"date": "2025-11-19",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "European Commission (EU)",
"title": "European Commission proposes the Digital Omnibus on AI regulation",
"event": "Proposed the Digital Omnibus (AI) regulation — the date of the Commission's proposal",
"significance": "The start of discussions on postponing and simplifying high-risk obligations",
"source_url": "https://www.europarl.europa.eu/legislative-train/package-digital-package/file-digital-omnibus-on-ai",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0766",
"date": "2025-11-19",
"date_precision": "day",
"axis": "19",
"axis_name": "On-Device & Local Inference",
"also_axes": "",
"actor": "Apple",
"title": "Apple publishes M5 GPU Neural Accelerator + MLX benchmarks",
"event": "Published M5 GPU Neural Accelerator + MLX benchmarks — time to first token (TTFT) for LLM inference up to 4× faster than M4, memory bandwidth 153GB/s",
"significance": "Material in which a consumer silicon generation change began to be promoted directly by local LLM inference performance",
"source_url": "https://machinelearning.apple.com/research/exploring-llms-mlx-m5",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0767",
"date": "2025-11-20",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "",
"actor": "Google",
"title": "Nano Banana Pro (Gemini 3 Pro Image)",
"event": "Nano Banana Pro (Gemini 3 Pro Image)",
"significance": "Accurate infographics and multilingual text rendering redefined image generation as a reasoning task; ships with SynthID",
"source_url": "https://blog.google/technology/ai/nano-banana-pro/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0768",
"date": "2025-11-20",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "",
"actor": "AI2",
"title": "Olmo 3",
"event": "Olmo 3 — released the Dolma 3 pretraining and Dolci post-training suites",
"significance": "Released the data and the post-training pipeline together",
"source_url": "https://allenai.org/blog/olmo3",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0769",
"date": "2025-11-20",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "Agility Robotics",
"title": "Digit moves more than 100,000 totes at a GXO logistics center",
"event": "Digit moved more than 100,000 totes cumulatively at a GXO logistics center",
"significance": "Disclosure of cumulative results on a commercial line (vendor's own report)",
"source_url": "https://www.agilityrobotics.com/content/digit-moves-over-100k-totes",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0770",
"date": "2025-11-21",
"date_precision": "day",
"axis": "4",
"axis_name": "MCP & Skills Standardization",
"also_axes": "",
"actor": "Anthropic, OpenAI, MCP-UI (jointly)",
"title": "Anthropic, OpenAI and MCP-UI propose the MCP Apps extension (SEP-1865)",
"event": "Proposed the MCP Apps extension (SEP-1865)",
"significance": "Lets servers provide interactive UIs via the ui:// scheme and sandboxed iframes; co-authored by competing camps",
"source_url": "https://blog.modelcontextprotocol.io/posts/2025-11-21-mcp-apps/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0771",
"date": "2025-11-23",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "Anthropic (MacDiarmid, Hubinger et al.)",
"title": "Natural Emergent Misalignment from Reward Hacking",
"event": "Natural Emergent Misalignment from Reward Hacking",
"significance": "Reward hacking in production RL spread into broad misalignment in unrelated areas; proposed inoculation prompting as a mitigation",
"source_url": "https://arxiv.org/abs/2511.18397",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0772",
"date": "2025-11-24",
"date_precision": "day",
"axis": "4",
"axis_name": "MCP & Skills Standardization",
"also_axes": "",
"actor": "Anthropic",
"title": "Anthropic releases advanced tool use in beta",
"event": "advanced tool use beta — Tool Search Tool, Programmatic Tool Calling",
"significance": "Cut tool tokens by 85% while MCP evaluation accuracy rose from 49% to 74% on Opus 4 — the paradox that showing fewer tools makes the model more accurate",
"source_url": "https://www.anthropic.com/engineering/advanced-tool-use",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0773",
"date": "2025-11-24",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "1",
"actor": "Anthropic",
"title": "Anthropic reports prompt injection defense results (Claude for Chrome)",
"event": "Prompt injection defense results (Claude for Chrome)",
"significance": "About 1% attack success rate against adaptive attacks. Stated explicitly that no browser agent is immune and that it does not claim the problem is solved",
"source_url": "https://www.anthropic.com/research/prompt-injection-defenses",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1253",
"date": "2025-11-24",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "13",
"actor": "Anthropic",
"title": "Anthropic releases Claude Opus 4.5",
"event": "Claude Opus 4.5 released — $5/$25 per 1M tokens (Opus 4 and 4.1: $15/$75), with a new effort parameter in the API",
"significance": "Company-reported: at medium effort it matches Sonnet 4.5’s best SWE-bench Verified score while using 76% fewer output tokens",
"source_url": "https://www.anthropic.com/news/claude-opus-4-5",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E0774",
"date": "2025-11-25",
"date_precision": "day",
"axis": "4",
"axis_name": "MCP & Skills Standardization",
"also_axes": "",
"actor": "Model Context Protocol",
"title": "Model Context Protocol revises its spec",
"event": "Spec revision — OIDC Discovery support, incremental scope consent via WWW-Authenticate, OAuth Client ID Metadata Documents recommended, URL mode elicitation, experimental tasks, formalized governance and working groups",
"significance": "Aligned the auth model with standard OAuth practice and moved spec governance to committee procedures",
"source_url": "https://modelcontextprotocol.io/specification/2025-11-25/changelog",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0775",
"date": "2025-11-25",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "",
"actor": "Black Forest Labs",
"title": "FLUX.2",
"event": "FLUX.2 — dev 32B with open weights",
"significance": "Combines a Mistral-3 24B VLM with a rectified flow transformer; a release in which open weights narrowed the quality gap",
"source_url": "https://bfl.ai/blog/flux-2",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0776",
"date": "2025-11-25",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Ilya Sutskever",
"title": "Ilya Sutskever says 2020–2025 was the age of scaling and research is back",
"event": "On Dwarkesh Podcast, diagnosed 2020 to 2025 as the age of scaling and said we are now back in the age of research, just with big computers. Named generalization as the key bottleneck and gave an AGI timeline of 5–20 years",
"significance": "The shift from the age of scaling to the age of research became the dominant frame of 2026 discourse",
"source_url": "https://www.dwarkesh.com/p/ilya-sutskever-2",
"source_type": "secondary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0777",
"date": "2025-11-26",
"date_precision": "day",
"axis": "5",
"axis_name": "Loop & Harness Engineering",
"also_axes": "",
"actor": "Anthropic (Justin Young)",
"title": "Effective harnesses for long-running agents",
"event": "Effective harnesses for long-running agents",
"significance": "Named the Claude Agent SDK explicitly as a general-purpose agent harness; the turning point at which a frontier lab adopted harness as a formal term",
"source_url": "https://www.anthropic.com/engineering/effective-harnesses-for-long-running-agents",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0778",
"date": "2025-11-28",
"date_precision": "day",
"axis": "19",
"axis_name": "On-Device & Local Inference",
"also_axes": "11",
"actor": "Liquid AI",
"title": "Liquid AI publishes the LFM2 technical report",
"event": "Published the LFM2 technical report — expanded to a 350M–8.3B family (dense 350M, 700M, 1.2B, 2.6B; MoE 8.3B total / 1.5B active), pretrained on 10–12T tokens, 32K context. Describes the design as coming from a hardware-in-the-loop architecture search under edge latency and memory constraints. LFM2-2.6B scored 79.56% on IFEval and 82.41% on GSM8K",
"significance": "A follow-up document to the July release. A case of an on-device family having public documentation at the level of a technical report",
"source_url": "https://arxiv.org/abs/2511.23404",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W36"
},
{
"id": "E0779",
"date": "2025-11-30",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "7",
"actor": "vLLM project",
"title": "vLLM project announces vLLM-Omni",
"event": "Announced vLLM-Omni — an omni-modal serving framework that separates encoders, the LLM core and modality generators (supports Qwen-Omni, Qwen-Image and others)",
"significance": "A sign that open-source serving infrastructure, once text-only, is expanding to cover non-autoregressive and multimodal generation",
"source_url": "https://vllm.ai/blog/2025-11-30-vllm-omni",
"source_type": "primary",
"claim_type": "community",
"added_week": "2026-W35"
},
{
"id": "E0780",
"date": "2025-12",
"date_precision": "month",
"axis": "5",
"axis_name": "Loop & Harness Engineering",
"also_axes": "",
"actor": "Anthropic",
"title": "Anthropic ships an official ralph wiggum plugin",
"event": "Shipped an official ralph wiggum plugin",
"significance": "A vendor making a community-born loop pattern official",
"source_url": "https://www.humanlayer.dev/blog/brief-history-of-ralph",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0781",
"date": "2025-12",
"date_precision": "month",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "The 5 elite teams of Korea's Independent AI Foundation Model project",
"title": "Korea's 5 elite foundation model teams release their first models together",
"event": "Released their first models at the same time — SKT A.X K1 (519B), LG K-EXAONE (236B), Naver Cloud HyperCLOVA X SEED, NC AI VAETKI (113B), Upstage Solar Open 100B",
"significance": "The moment large Korean open-weight models appeared all at once",
"source_url": "https://www.aitimes.com/news/articleView.html?idxno=205195",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0782",
"date": "2025-12",
"date_precision": "month",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Nous Research",
"title": "Hermes 4.3 36B",
"event": "Hermes 4.3 36B — based on the ByteDance Seed 36B base",
"significance": "A US community team fine-tuning on top of a Chinese base model — a symbolic scene of shifting leadership",
"source_url": "https://huggingface.co/NousResearch/Hermes-4.3-36B",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0783",
"date": "2025-12-01",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "",
"actor": "Nous Research",
"title": "Nous Research releases Hermes 4.3 36B",
"event": "Released Hermes 4.3 36B — trained the same model both centrally (FSDP+AdamW) and on Psyche (TP+DisTrO) and compared them. The Psyche run completed at an average of 144k tok/s on 24 nodes and beat the centralized run on downstream performance",
"significance": "The first controlled comparison showing distributed training is not just possible but equal or better",
"source_url": "https://nousresearch.com/introducing-hermes-4-3/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1254",
"date": "2025-12-01",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "6",
"actor": "DeepSeek",
"title": "DeepSeek releases DeepSeek-V3.2 and V3.2-Speciale",
"event": "Released DeepSeek-V3.2 and V3.2-Speciale — the official successor to V3.2-Exp, with thinking during tool use; weights for both under MIT (Speciale offered on a temporary API endpoint until December 15)",
"significance": "Open-weight reasoning models; DeepSeek claimed V3.2-Speciale reached gold-level results at IMO, IOI and other 2025 competitions",
"source_url": "https://www.deepseek.com/en/news/deepseek-v3-2",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E0784",
"date": "2025-12-02",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Mistral AI",
"title": "Mistral AI releases the Mistral 3 family",
"event": "Released the Mistral 3 family — Mistral Large 3 (675B total / 41B active MoE), Ministral 3 14B/8B/3B. All models under Apache 2.0",
"significance": "Released a 675B-class frontier model under Apache 2.0. The largest fully open release in the West as of late 2025",
"source_url": "https://mistral.ai/news/mistral-3/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0785",
"date": "2025-12-04",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "2",
"actor": "Google Research",
"title": "Google Research introduces Titans and MIRAS, the framework that generalizes it",
"event": "Introduced Titans and MIRAS, a theoretical framework that generalizes it — a neural long-term memory architecture",
"significance": "The route of working around long-context limits through architecture rather than retrieval",
"source_url": "https://research.google/blog/titans-miras-helping-ai-have-long-term-memory/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0786",
"date": "2025-12-05",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "ARC Prize",
"title": "ARC Prize 2025 results",
"event": "ARC Prize 2025 results — Kaggle winner 24.03%, Claude Opus 4.5 37.6%, Poetiq refinement loop 54%",
"significance": "Showed that the key technical change of 2025 was iterative loops of search and checking",
"source_url": "https://arcprize.org/blog/arc-prize-2025-results-analysis",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0787",
"date": "2025-12-09",
"date_precision": "day",
"axis": "4",
"axis_name": "MCP & Skills Standardization",
"also_axes": "",
"actor": "Anthropic, Block, OpenAI",
"title": "Anthropic, Block and OpenAI donate MCP to the Linux Foundation's AAIF",
"event": "Donated MCP to the AAIF under the Linux Foundation",
"significance": "The founding projects were MCP, goose and AGENTS.md, at a point when public MCP servers exceeded 10,000",
"source_url": "https://blog.modelcontextprotocol.io/posts/2025-12-09-mcp-joins-agentic-ai-foundation/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0788",
"date": "2025-12-09",
"date_precision": "day",
"axis": "5",
"axis_name": "Loop & Harness Engineering",
"also_axes": "",
"actor": "Linux Foundation",
"title": "Linux Foundation launches the Agentic AI Foundation",
"event": "Launched the Agentic AI Foundation — with MCP, goose and AGENTS.md contributed",
"significance": "Harness interfaces moved from objects of vendor competition to shared infrastructure; AGENTS.md adopted by 60,000+ projects",
"source_url": "https://www.linuxfoundation.org/press/linux-foundation-announces-the-formation-of-the-agentic-ai-foundation",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0789",
"date": "2025-12-10",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "",
"actor": "OLG Hamburg",
"title": "Hamburg Higher Regional Court dismisses the appeal in Kneschke v. LAION",
"event": "Dismissed the appeal in Kneschke v. LAION",
"significance": "Explicitly applied the commercial TDM exception and found that a natural-language opt-out did not meet the machine-readability requirement. Not final, as an appeal to the BGH was permitted",
"source_url": "https://www.technologyslegaledge.com/2025/12/robert-kneschke-v-laion-judgment-of-10-december-2025-ref-5-u-104-24/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0790",
"date": "2025-12-11",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "White House (US)",
"title": "White House signs Executive Order 14365 on a national AI policy framework",
"event": "Signed Executive Order 14365, Ensuring a National Policy Framework for Artificial Intelligence — an AI Litigation Task Force under the Attorney General to try to invalidate state laws, a link to BEAD funding, and a recommendation for federal preemption legislation",
"significance": "The point at which the federal government and the states shifted from a hierarchical relationship to an adversarial one",
"source_url": "https://www.federalregister.gov/documents/2025/12/16/2025-23092/ensuring-a-national-policy-framework-for-artificial-intelligence",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0791",
"date": "2025-12-11",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "",
"actor": "Cohere",
"title": "Cohere launches Rerank 4",
"event": "Launched Rerank 4",
"significance": "Showed that, right after the view that embeddings have limits spread, the commercial importance of the reranking layer actually grew",
"source_url": "https://cohere.com/blog/rerank-4",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1255",
"date": "2025-12-11",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "OpenAI",
"title": "OpenAI releases GPT-5.2",
"event": "GPT-5.2 released — Instant, Thinking and Pro, pitched as a frontier model for professional knowledge work and long-running agents",
"significance": "Company-reported for GPT-5.2 Thinking: GDPval wins or ties 70.9% (GPT-5: 38.8%), ARC-AGI-2 (Verified) 52.9% (GPT-5.1 Thinking: 17.6%), SWE-bench Verified 80.0%",
"source_url": "https://openai.com/index/introducing-gpt-5-2/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E0792",
"date": "2025-12-13",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Wei Kai (魏凯, Director of the AI Research Institute, China Academy of Information and Communications Technology)",
"title": "CAICT's Wei Kai says agents show an early form of digital labor",
"event": "Said agents have become the main form of large-model applications and are showing an early form of digital labor, and that safety governance has become another main line running in parallel with technological development. Pointed out that the speed of AI's penetration into industry is constrained by how hard it is to obtain industrial data, how well process knowledge is encapsulated, and reliability requirements",
"significance": "A Chinese state-affiliated think tank officially raised safety governance to an axis on par with technological development. Its diagnosis that the bottlenecks to penetrating manufacturing are data, process knowledge and reliability matches how things look on the OSAT floor",
"source_url": "https://www.21jingji.com/article/20251214/herald/23d536044b8ceaab5017d55aea27db46.html",
"source_type": "secondary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0793",
"date": "2025-12-16",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "METR",
"title": "Common Elements of Frontier AI Safety Policies",
"event": "Common Elements of Frontier AI Safety Policies",
"significance": "Laid out 9 common elements across 12 companies' policies and became a de facto quasi-standard document for the industry",
"source_url": "https://metr.org/common-elements",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0794",
"date": "2025-12-16",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "High Court of England and Wales (Justice Smith)",
"title": "Getty gets permission to appeal on secondary infringement; Stability is refused",
"event": "Granted Getty permission to appeal the part of Getty v Stability it lost on secondary infringement, and refused Stability permission to appeal on trademarks",
"significance": "A follow-up step showing that the Nov 4, 2025 judgment on the merits is not final. It will be fought again on appeal",
"source_url": "https://ipkitten.blogspot.com/2026/01/permission-to-appeal-granted-in-getty.html",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0795",
"date": "2025-12-17",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "NVIDIA",
"title": "NVIDIA applies VLMs and VFMs to semiconductor defect classification",
"event": "Applied VLMs and VFMs to semiconductor defect classification — PCB from 93.84% to 98.51%",
"significance": "A combination of about 600 labeled images and domain adaptation on about 1 million unlabeled PCB images. The label efficiency has direct implications for the OSAT floor",
"source_url": "https://developer.nvidia.com/blog/optimizing-semiconductor-defect-classification-with-generative-ai-and-vision-foundation-models/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0796",
"date": "2025-12-18",
"date_precision": "day",
"axis": "4",
"axis_name": "MCP & Skills Standardization",
"also_axes": "",
"actor": "Anthropic",
"title": "Anthropic publishes Agent Skills as an open standard (agentskills.io)",
"event": "Published Agent Skills as an open standard (agentskills.io)",
"significance": "Released a specification and a reference SDK; the second standardization attempt after MCP",
"source_url": "https://github.com/agentskills/agentskills",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0797",
"date": "2025-12-19",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Kakao",
"title": "Kakao open-sources Kanana-2 (Base/Instruct/Thinking)",
"event": "Released Kanana-2 (Base/Instruct/Thinking) as open source",
"significance": "Optimized for agentic AI. The successor in the Kanana-1.5 lineage",
"source_url": "https://www.kakaocorp.com/page/detail/11854",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0798",
"date": "2025-12-19",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "7",
"actor": "Yann LeCun",
"title": "Yann LeCun confirms he has left Meta to found AMI Labs",
"event": "Confirmed he has left Meta and founded AMI Labs (Advanced Machine Intelligence Labs) — himself as Executive Chairman, Alex LeBrun as CEO",
"significance": "With an independent lab pursuing world models rather than LLMs, the landscape of the discourse split",
"source_url": "https://techcrunch.com/2025/12/19/yann-lecun-confirms-his-new-world-model-startup-reportedly-seeks-5b-valuation",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0799",
"date": "2025-12-23",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "Government of Japan (Cabinet)",
"title": "Japan's Cabinet adopts the AI Basic Plan",
"event": "Cabinet decision adopting the AI Basic Plan — 3 principles: balancing the promotion of innovation with risk management, agile response, and integrated domestic and international policy. It set the goal of becoming the country where AI is easiest to develop and use in the world, and called for expanding the AI Safety Institute's staff to about 2 times the current level",
"significance": "The first statutory basic plan under the AI Promotion Act. Japan locks in a minimal-regulation, maximal-use position in a national document",
"source_url": "https://www8.cao.go.jp/cstp/ai/ai_plan/aiplan_20251223.pdf",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0800",
"date": "2025-12-24",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "",
"actor": "NVIDIA, Groq",
"title": "NVIDIA takes non-exclusive Groq inference license, hires Jonathan Ross and team",
"event": "Non-exclusive licensing of inference technology, with key staff including Jonathan Ross brought on board",
"significance": "Non-GPU inference IP is absorbed. Groq continues as an independent company",
"source_url": "https://groq.com/newsroom/groq-and-nvidia-enter-non-exclusive-inference-technology-licensing-agreement-to-accelerate-ai-inference-at-global-scale",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0801",
"date": "2025-12-28",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Kerem Zaman et al. (UNC Chapel Hill)",
"title": "Is Chain-of-Thought Really Not Explainability?",
"event": "Published \"Is Chain-of-Thought Really Not Explainability?\" — a rebuttal arguing that CoT can be faithful even when it does not put the hint into words",
"significance": "A direct counterargument to the Oxford position paper. Cite the two as a pair",
"source_url": "https://arxiv.org/abs/2512.23032",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0802",
"date": "2025-12-30",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "xAI",
"title": "xAI publishes its Frontier Artificial Intelligence Framework",
"event": "Published the xAI Frontier Artificial Intelligence Framework — a document for compliance with California's TFAIA",
"significance": "A product of the first cycle in which state law began requiring frontier labs to publish their safety frameworks",
"source_url": "https://data.x.ai/2025-12-31-xai-frontier-artificial-intelligence-framework.pdf",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0803",
"date": "2025-12-31",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "",
"actor": "Google (Behrouz et al.)",
"title": "Nested Learning / HOPE",
"event": "Nested Learning / HOPE",
"significance": "Recasts the optimizer as an associative memory module and proposes a sequence model that learns a self-update algorithm",
"source_url": "https://arxiv.org/abs/2512.24695",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0804",
"date": "2026",
"date_precision": "year",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "Amkor Technology",
"title": "Amkor says it uses machine learning to improve detection of hard-to-spot defects",
"event": "States on its own smart manufacturing page that it applies machine learning techniques to improve the detection of defects that are hard for people to tell apart. Quantitative figures: 0; measuring party: not stated; mass production vs. pilot: not distinguished",
"significance": "The No. 2 OSAT discloses at the same level. Back-end AI does not lack applications; it lacks quantitative disclosure, and this gap is the real reason internal adoption is hard to justify",
"source_url": "https://amkor.com/smart-manufacturing/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0805",
"date": "2026-01",
"date_precision": "month",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "IBM (Raffi Khatchadourian)",
"title": "Replayable Financial Agents",
"event": "Published \"Replayable Financial Agents\" — 4,705 agent runs at temperature 0.0 across 7 models and 4 providers. By the author's measurement, decision reproducibility showed no correlation with task accuracy (r=-0.11, p=0.63). Large models of 120B+ had reproducibility under 20%",
"significance": "The domain is finance, but the requirement to replay for audits has the same structure as quality audits. Refutes, with 4,705 runs, the common belief that picking a more accurate model brings reproducibility along with it",
"source_url": "https://arxiv.org/html/2601.15322v2",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0806",
"date": "2026-01",
"date_precision": "month",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "Music publishers (Concord and others)",
"title": "Music publishers file a new lawsuit against Anthropic",
"event": "Filed a new lawsuit against Anthropic — covering more than 20,000 songs and claiming more than $3 billion. The original October 2023 suit continues, having gone through a January 2025 agreement to keep guardrails in place and the March 2025 denial of a preliminary injunction",
"significance": "A training-data dispute over lyrics and music",
"source_url": "https://www.musicbusinessworldwide.com/music-publishers-file-amended-lyrics-lawsuit-against-anthropic-just-as-ai-firms-separate-1-5b-piracy-settlement-with-authors-wins-court-approval/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0807",
"date": "2026-01",
"date_precision": "month",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "10",
"actor": "Dario Amodei",
"title": "The Adolescence of Technology",
"event": "Posted \"The Adolescence of Technology\" — assesses that in 2026 we are considerably closer to real danger than in 2023, and sets out 5 categories: autonomy risks, destructive misuse, seizure of power, economic collapse, and indirect effects. At the same time, it cautions against quasi-religious doomerism",
"significance": "Greatly expands the account of risk without retracting the optimism of \"Machines of Loving Grace\". It leaves two texts by the same author being cited on both the optimistic and the risk side",
"source_url": "https://www.darioamodei.com/essay/the-adolescence-of-technology",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0808",
"date": "2026-01-03",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Yann LeCun",
"title": "LeCun interview after leaving Meta to found Advanced Machine Intelligence Labs",
"event": "Interview after leaving Meta and founding Advanced Machine Intelligence Labs — said there are probably many people at Meta who would rather he not tell the world that LLMs are basically a dead end, and that you cannot tell a researcher like him what to do",
"significance": "A case in which a split over direction ended in an actual divergence of capital and organization. A V-JEPA-based world model secured independent funding",
"source_url": "https://the-decoder.com/you-certainly-dont-tell-a-researcher-like-me-what-to-do-says-lecun-as-he-exits-meta-for-his-own-startup/",
"source_type": "secondary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0809",
"date": "2026-01-05",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "Yi Yu et al.",
"title": "Agentic Memory",
"event": "Published \"Agentic Memory\" — exposes storing, retrieving, updating, summarizing and discarding as tool calls, and trains them with step-wise reinforcement learning",
"significance": "A case in which what to remember and what to discard is learned rather than decided by people",
"source_url": "https://arxiv.org/abs/2601.01885",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0810",
"date": "2026-01-05",
"date_precision": "day",
"axis": "5",
"axis_name": "Loop & Harness Engineering",
"also_axes": "2",
"actor": "Philipp Schmid",
"title": "The importance of Agent Harness in 2026",
"event": "\"The importance of Agent Harness in 2026\" — the analogy of model = CPU, context = RAM, harness = OS",
"significance": "The primary source for the line of definitions that treats the harness as an operating system",
"source_url": "https://www.philschmid.de/agent-harness-2026",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0811",
"date": "2026-01-05",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "Hyundai, Boston Dynamics (CES)",
"title": "Hyundai plans to produce 30,000 Atlas robots a year (2028, Georgia)",
"event": "Plan to produce 30,000 Atlas units a year (2028, Georgia)",
"significance": "An automaker group presents a vertical-integration model (planned figures)",
"source_url": "https://www.axios.com/2026/01/05/hyundai-humanoid-robots-boston-dynamics",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0812",
"date": "2026-01-06",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "xAI",
"title": "xAI raises $20 billion Series E",
"event": "$20 billion Series E. Valor, StepStone, Fidelity, QIA, MGX, NVIDIA and Cisco Investments participated",
"significance": "Among the largest single rounds for an AI startup. The valuation was not disclosed in the announcement",
"source_url": "https://x.ai/news/series-e",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0813",
"date": "2026-01-06",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "Siemens",
"title": "Siemens unveils Industrial AI OS and 9 industrial copilots at CES 2026",
"event": "At CES 2026, announced an Industrial AI OS (with NVIDIA) and 9 industrial copilots for Teamcenter and Opcenter. The first adaptive manufacturing site is its own factory. The quantitative case presented comes from a food-industry site, not semiconductors",
"significance": "A candidate for back-end use too, for querying MES documents and SOPs, but all published results are non-semiconductor and the measuring party is the vendor",
"source_url": "https://press.siemens.com/global/en/pressrelease/siemens-unveils-technologies-accelerate-industrial-ai-revolution-ces-2026",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0814",
"date": "2026-01-08",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Z.ai (formerly Zhipu AI)",
"title": "Z.ai lists on the Hong Kong Stock Exchange",
"event": "Listed on the Hong Kong Stock Exchange — the world's first listed LLM company",
"significance": "Open-weight distribution is endorsed as a business model fit for a public listing",
"source_url": "https://www.prnewswire.com/news-releases/chinas-agi-pioneer-and-leader-zai-listed-on-hong-kong-stock-exchange-302656265.html",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0815",
"date": "2026-01-08",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "Zhipu AI (智谱)",
"title": "Zhipu AI lists on the Hong Kong exchange",
"event": "Listed on the Hong Kong exchange — market cap HK$52.8 billion, more than HK$4.3 billion raised, public offering oversubscribed 1,159 times",
"significance": "The first entry of a pure-play LLM company into the public markets. A turning point at which Chinese labs' fundraising shifted from VC to public offerings",
"source_url": "https://www.qbitai.com/2026/01/367379.html",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0816",
"date": "2026-01-09",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "Elias Lumer et al.",
"title": "Don't Break the Cache",
"event": "Published \"Don't Break the Cache\" — measured prompt caching in the APIs of 3 providers: cost savings of 41–80%, though latency can increase depending on the placement strategy",
"significance": "The first measurement comparing the 3 providers under identical conditions since caching became a product",
"source_url": "https://arxiv.org/abs/2601.06007",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0817",
"date": "2026-01-09",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "MiniMax",
"title": "MiniMax lists on the Hong Kong exchange",
"event": "Listed on the Hong Kong exchange — raised about HK$5.54 billion; intraday market cap exceeded HK$76.3 billion on the first day of trading",
"significance": "A follow-on listing one day after Zhipu's. Confirms that the listing window for Chinese LLM companies has opened",
"source_url": "https://cn.technode.com/post/2026-01-09/minimax-hkex-listing-largest-ai-model-ipo/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0818",
"date": "2026-01-11",
"date_precision": "day",
"axis": "19",
"axis_name": "On-Device & Local Inference",
"also_axes": "16",
"actor": "Uygar Kurt",
"title": "Uygar Kurt publishes a unified evaluation of llama.cpp quantization",
"event": "Published a paper evaluating llama.cpp quantization in a unified way — compares 3- to 8-bit K-quants and legacy formats across benchmarks, perplexity, throughput, file size and even quantization time",
"significance": "The first unified reference to move the choice of quantization from gut feel to measurement",
"source_url": "https://arxiv.org/abs/2601.14277",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0819",
"date": "2026-01-12",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "Nikhil Verma",
"title": "Active Context Compression",
"event": "Published \"Active Context Compression\" — the agent consolidates knowledge on its own and prunes the original context, cutting tokens by 22.7%",
"significance": "Part of a shift in who manages context: from the pipeline to the agent itself",
"source_url": "https://arxiv.org/abs/2601.07190",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0820",
"date": "2026-01-12",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "Pietro Ferrazzi et al.",
"title": "Is Agentic RAG worth it?",
"event": "Published \"Is Agentic RAG worth it?\" — compares enhanced RAG and agentic RAG across many scenarios",
"significance": "Empirical evidence that agentic RAG is not always better. A disconfirming result that grounds design choices",
"source_url": "https://arxiv.org/abs/2601.07711",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0821",
"date": "2026-01-14",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "California Attorney General",
"title": "California AG opens investigation into xAI/Grok non-consensual sexual deepfakes",
"event": "Opened a formal investigation into xAI/Grok's generation of non-consensual sexual deepfakes",
"significance": "At issue is the use of spicy mode in marketing. A case of a US state government directly investigating a frontier lab",
"source_url": "https://www.oag.ca.gov/news/press-releases/attorney-general-bonta-launches-investigation-xai-grok-over-undressed-sexual-ai",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0822",
"date": "2026-01-15",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "xAI",
"title": "xAI blocks revealing edits of real people, limits image generation to paid users",
"event": "Applied technical measures to block editing images of real people into revealing clothing, and restricted image generation and editing to paid subscribers",
"significance": "An after-the-fact response, limited in scope. AP found that the editing tool was still available to free users the day after the measures were announced — a gap between announcement and actual enforcement",
"source_url": "https://www.pbs.org/newshour/world/grok-blocked-from-undressing-images-with-ai-in-places-where-its-illegal-x-says",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0823",
"date": "2026-01-15",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "Chinese customs",
"title": "Chinese customs bars entry of H200 chips",
"event": "Instruction not to allow H200 imports",
"significance": "A two-way control dynamic: as the US opens, China closes",
"source_url": "https://www.taipeitimes.com/News/biz/archives/2026/01/15/2003850605",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0824",
"date": "2026-01-15",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "BIS",
"title": "BIS moves H200-class China exports from presumption of denial to case-by-case",
"event": "Shifted exports to China of H200-class chips (TPP under 21,000, DRAM bandwidth under 6,500 GB/s) from a presumption of denial to case-by-case review",
"significance": "The peak of the regulatory reversal. 5 conditions imposed",
"source_url": "https://www.federalregister.gov/documents/2026/01/15/2026-00789/revision-to-license-review-policy-for-advanced-computing-commodities",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0825",
"date": "2026-01-15",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "Ministry of Science and ICT",
"title": "Korea announces first-round results of independent AI foundation model project",
"event": "Announced the first-round evaluation results of the independent AI foundation model project — LG AI Research, SKT and Upstage advanced to the second round; Naver Cloud and NC AI were eliminated",
"significance": "The first eliminations in Korea's largest government-funded AI program",
"source_url": "https://www.epnc.co.kr/news/articleView.html?idxno=327342",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0826",
"date": "2026-01-15",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "World Economic Forum (with McKinsey)",
"title": "WEF names 23 new Global Lighthouse Network sites",
"event": "Announced 23 new Global Lighthouse Network sites. By its tally, 94% of successful transformations combine multiple technology domains, and deploying AI on its own is rare. None of the 23 new sites is semiconductor-specific, and none is in Korea",
"significance": "Quasi-standard evidence, tallied across 220 sites, that adopting AI on its own does not deliver results. On the other hand, the absence of back-end sites signals that this benchmark is hard to apply directly to OSATs",
"source_url": "https://www.weforum.org/press/2026/01/global-lighthouse-network-recognizes-23-new-sites-launches-ai-platform-for-industrial-transformation/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0827",
"date": "2026-01-15",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "",
"actor": "Voyage AI (MongoDB)",
"title": "Voyage AI launches the Voyage 4 model family",
"event": "Launched the Voyage 4 model family — MoE applied to large, the 4 models share one embedding space, and nano is under Apache 2.0",
"significance": "Applies MoE to commercial embeddings and standardizes embedding compatibility across models of different sizes. Comparison figures are the vendor's own measurements",
"source_url": "https://blog.voyageai.com/2026/01/15/voyage-4/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0828",
"date": "2026-01-21",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "",
"actor": "PyTorch Foundation",
"title": "PyTorch Foundation releases PyTorch 2.10",
"event": "PyTorch 2.10 released — varlen_attn() variable-length attention (forward and backward, A100+ BF16/FP16), torchinductor combo-kernel horizontal fusion, cuSOLVER DnXgeev general eigenvalue decomposition, torch.compile honoring use_deterministic_mode and the introduction of DebugMode, and Python 3.14 support. Announced that TorchScript will be replaced by torch.export. 536 contributors and 4,160 commits since 2.9. From 2026 the release cycle shortens from quarterly to every 2 months",
"significance": "The point at which numerical reproducibility (determinism, tensor-hashing debugging) made the release headline. A sign that the training stack is moving from a performance race to a debuggability phase",
"source_url": "https://pytorch.org/blog/pytorch-2-10-release-blog/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W36"
},
{
"id": "E0829",
"date": "2026-01-22",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "Anthropic",
"title": "Anthropic releases a fully revised Claude's Constitution",
"event": "A fully revised edition of Claude's Constitution",
"significance": "Shifts from a list of principles to a document that helps Claude understand the reasons behind them; spells out 4 core priorities and hard constraints",
"source_url": "https://www.anthropic.com/news/claude-new-constitution",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0830",
"date": "2026-01-22",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "Republic of Korea",
"title": "Korea's AI Basic Act and its Enforcement Decree take effect",
"event": "The AI Basic Act and its Enforcement Decree take effect — effective date. Article 31 labeling of generative AI, Article 32 safety assurance (cumulative training compute of 10^26 FLOP or more), Articles 33–34 high-impact AI, Article 36 domestic representative",
"significance": "However, fact-finding investigations and fines come with a guidance period of at least 1 year, so statements that penalties begin in 2026 are inaccurate",
"source_url": "https://www.lawtimes.co.kr/news/articleView.html?idxno=216500",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0831",
"date": "2026-01-22",
"date_precision": "day",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
"also_axes": "",
"actor": "METR",
"title": "METR publishes an official note on the limits of its time horizon metric",
"event": "Published an official note setting out the limitations of the time horizon metric — states an error range of about 2x and that the task distribution is unclear",
"significance": "A case of the issuing organization itself putting the brakes on its own metric being over-generalized when cited",
"source_url": "https://metr.org/notes/2026-01-22-time-horizon-limitations/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0832",
"date": "2026-01-23",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "Attorneys general of 35 US states and territories",
"title": "Attorneys general of 35 US states and territories send joint letter to xAI",
"event": "Sent a joint letter to xAI — demanding that it block NCII generation, delete content already generated, suspend users who generate it, and give users the right to refuse Grok edits",
"significance": "The largest coordinated action by US state law enforcement against a single AI product",
"source_url": "https://ag.ny.gov/sites/default/files/letters/multistate-letter-to-xai-letters-2026.pdf",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0833",
"date": "2026-01-26",
"date_precision": "day",
"axis": "4",
"axis_name": "MCP & Skills Standardization",
"also_axes": "",
"actor": "Anthropic, MCP",
"title": "Claude fully supports MCP Apps as an official extension",
"event": "Claude fully supports MCP Apps as an official extension",
"significance": "A protocol that used to carry only text and structured data now extends to on-screen interfaces",
"source_url": "https://blog.modelcontextprotocol.io/posts/2026-01-26-mcp-apps/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0834",
"date": "2026-01-26",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "European Commission (EU)",
"title": "EU opens formal DSA probe into X's Grok, extends 2023 recommender probe",
"event": "Opened a formal DSA investigation into X's Grok and extended its 2023 investigation into recommender systems — this is the opening of an investigation, not a sanction",
"significance": "The first case of generative AI being regulated under the DSA rather than the AI Act",
"source_url": "https://digital-strategy.ec.europa.eu/en/news/commission-investigates-grok-and-xs-recommender-systems-under-digital-services-act",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0835",
"date": "2026-01-27",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "",
"actor": "Peter Steinberger",
"title": "Steinberger renames Clawdbot to Moltbot after Anthropic trademark objection",
"event": "Renamed Clawdbot to Moltbot following a trademark objection from Anthropic",
"significance": "A case where trademark law put a stop to the practice of open-source agents riding on a model provider's brand",
"source_url": "https://en.wikipedia.org/wiki/OpenClaw",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0836",
"date": "2026-01-27",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "Figure",
"title": "Helix 02",
"event": "Helix 02 — S0 whole-body balance at 1 kHz, S1 joints at 200 Hz, S2 semantic reasoning",
"significance": "Separates real-time response and intelligence by frequency band; shifts from upper-body control to whole-body control",
"source_url": "https://www.figure.ai/news/helix-02",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0837",
"date": "2026-01-28",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "",
"actor": "xAI",
"title": "xAI releases the Grok Imagine API",
"event": "Released the Grok Imagine API — text-to-video, image-to-video, and editing features such as object manipulation and restyling",
"significance": "Commercializes a video generation API in the midst of regulatory pressure over NCII. The announcement makes no mention of content safety or moderation",
"source_url": "https://x.ai/news/grok-imagine-api",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0838",
"date": "2026-01-28",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "1",
"actor": "Cisco (Amy Chang, Vineeth Sai Narajala, Idan Habler)",
"title": "Cisco finds 26% of 31,000 agent skills have at least one vulnerability",
"event": "An analysis of 31,000 agent skills found that 26% had at least one vulnerability. Documented silent data exfiltration, prompt injection, command injection and tool poisoning as types, and released an open-source Skill Scanner",
"significance": "The first large-scale measurement to treat skill marketplaces as an unvetted supply chain",
"source_url": "https://blogs.cisco.com/ai/personal-ai-agents-like-openclaw-are-a-security-nightmare",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0839",
"date": "2026-01-28",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "University of Ljubljana (Fučka, Zavrtanik, Skočaj)",
"title": "AnomalyVFM",
"event": "Published AnomalyVFM — turns vision foundation models into zero-shot anomaly detectors through 3-stage synthetic data generation and LoRA. By the authors' measurement, average image-AUROC of 94.1% across 9 industrial benchmarks (previous best 90.8%), with 0 in-domain training images",
"significance": "The only approach that covers the early phase of a new product line, when there are not even normal images. However, there are no semiconductor- or PCB-specific results, so an in-house pilot is needed to check it",
"source_url": "https://arxiv.org/html/2601.20524v1",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0840",
"date": "2026-01-28",
"date_precision": "day",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
"also_axes": "",
"actor": "LMArena",
"title": "LMArena renames itself Arena",
"event": "Renamed LMArena to Arena",
"significance": "Officially broadens its identity from a text LLM arena to a multi-modality evaluation platform",
"source_url": "https://arena.ai/blog/lmarena-is-now-arena",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0841",
"date": "2026-01-29",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "",
"actor": "OpenClaw",
"title": "Moltbot is renamed OpenClaw and launches an official blog",
"event": "Renamed again, from Moltbot to OpenClaw, and launched an official blog",
"significance": "The 3rd renaming in 2 months. A turning point that separates the project's identity from a personal brand",
"source_url": "https://openclaw.ai/blog/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0842",
"date": "2026-01-29",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "",
"actor": "Moonshot AI",
"title": "Moonshot AI releases Kimi K2.5",
"event": "Released Kimi K2.5 — MoE with 1T total / 32B active parameters, natively multimodal through pretraining on vision-language tokens, introduces a self-coordinating Agent Swarm, modified-MIT",
"significance": "The point where the K2 line shifted from text to native multimodality, and the arrival of an open-weight agent swarm",
"source_url": "https://huggingface.co/moonshotai/Kimi-K2.5",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0843",
"date": "2026-01-29",
"date_precision": "day",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
"also_axes": "5",
"actor": "METR",
"title": "METR releases Time Horizon 1.1",
"event": "Released Time Horizon 1.1 — tasks increased from 170 to 228; evaluation infrastructure switched from Vivaria to Inspect. Only 14 of the previous 33 models were re-evaluated. The doubling time for the period since 2024 went from 109 days to 89 days",
"significance": "A case of the issuing organization itself showing that a change in methodology alone moves the numbers. The top model's 50% time horizon of about 5.3 hours is based on this revision and cannot be compared directly with the previous version",
"source_url": "https://metr.org/blog/2026-1-29-time-horizon-1-1/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0844",
"date": "2026-01-30",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "OpenClaw / depthfirst (Mav Levin)",
"title": "OpenClaw ships a fix for CVE-2026-25253",
"event": "Shipped a fix for CVE-2026-25253 — the Control UI accepted unauthenticated WebSocket connections and trusted gatewayUrl without validation, enabling token theft followed by one-click RCE (CVSS 8.8)",
"significance": "The first major incident to shatter the assumption that local agents are safe. Origin validation has since become a baseline requirement",
"source_url": "https://thehackernews.com/2026/02/openclaw-bug-enables-one-click-remote.html",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0845",
"date": "2026-01-30",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "Community (Steinberger)",
"title": "OpenClaw name settled (Warelay→Clawdbot→Moltbot→OpenClaw)",
"event": "OpenClaw name finalized (Warelay→Clawdbot→Moltbot→OpenClaw)",
"significance": "The permission and audit problems of personal autonomous agents escalate into a public controversy",
"source_url": "https://en.wikipedia.org/wiki/OpenClaw",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0846",
"date": "2026-02",
"date_precision": "month",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Alibaba/Qwen",
"title": "Alibaba releases Qwen3.5-397B-A17B as open weights",
"event": "Released Qwen3.5-397B-A17B as open weights — Gated Delta Network + sparse MoE combined with a vision encoder, context of 262,144 (1.01 million with YaRN), 201 languages, Apache 2.0",
"significance": "Applies early-fusion multimodal training to a large Apache 2.0 MoE. Plus remains closed, so open and closed run in parallel",
"source_url": "https://huggingface.co/Qwen/Qwen3.5-397B-A17B",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0847",
"date": "2026-02",
"date_precision": "month",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Yoshua Bengio",
"title": "Yoshua Bengio chairs the second International AI Safety Report",
"event": "Published the second International AI Safety Report (as chair) — backed by the governments of more than 30 countries and international organizations, with more than 100 experts taking part. The 2026 edition focuses on how sophisticated attackers can get around current defenses and on the uncertain effectiveness of many safeguards",
"significance": "Brings the format of an IPCC-style international consensus document to the AI risk debate. A shared factual basis for regulatory debates in each country",
"source_url": "https://internationalaisafetyreport.org/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0848",
"date": "2026-02-02",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "xAI",
"title": "SpaceX acquires xAI",
"event": "SpaceX acquired xAI — valuing SpaceX at $1 trillion and xAI at $250 billion",
"significance": "The largest merger on record. The same document lists xAI's September-quarter revenue at $107 million and net loss at $1.46 billion, a wide gap between valuation and results. The company name was later written as SpaceXAI",
"source_url": "https://x.ai/news/xai-joins-spacex",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0849",
"date": "2026-02-03",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Xi Wang et al.",
"title": "Conformal Thinking",
"event": "Published \"Conformal Thinking\" — recasts the allocation of reasoning compute budgets as a conformal risk control problem",
"significance": "Formalizes budget-forcing (s1)-style control of reasoning budgets within a framework of statistical guarantees",
"source_url": "https://arxiv.org/abs/2602.03814",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0850",
"date": "2026-02-03",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "UK ICO",
"title": "UK ICO opens formal investigation into XIUC and X.AI",
"event": "Opened a formal investigation into XIUC and X.AI — examining whether Grok's design built in safeguards against the risk of generating synthetic sexual images. In coordination with Ofcom",
"significance": "A case of a data protection regulator making the model's design itself the subject of review",
"source_url": "https://www.hunton.com/privacy-and-cybersecurity-law-blog/uk-ico-opens-inquiry-into-grok-ai-over-non-consensual-sexual-imagery-allegations",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0851",
"date": "2026-02-04",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Donald Ye et al.",
"title": "Mechanistic Evidence for Faithfulness Decay in Chain-of-Thought Reasoning",
"event": "Published \"Mechanistic Evidence for Faithfulness Decay in Chain-of-Thought Reasoning\" — beyond the point at 70–85% of the chain's length, the contribution of reasoning tokens drops sharply",
"significance": "Adds mechanism-level evidence to the CoT faithfulness debate",
"source_url": "https://arxiv.org/abs/2602.11201",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0852",
"date": "2026-02-05",
"date_precision": "day",
"axis": "5",
"axis_name": "Loop & Harness Engineering",
"also_axes": "",
"actor": "Mitchell Hashimoto",
"title": "My AI Adoption Journey",
"event": "\"My AI Adoption Journey\" — gives harness engineering its name",
"significance": "The starting point for the term's spread; step 5 of the 6 steps of AI adoption is Engineer the Harness",
"source_url": "https://mitchellh.com/writing/my-ai-adoption-journey",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0853",
"date": "2026-02-05",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "Anthropic",
"title": "Claude Opus 4.6 system card",
"event": "Claude Opus 4.6 system card published — the model deployed under the ASL-3 Standard",
"significance": "Reports that the model is adept at distinguishing evaluations from real deployment but not consistently forthright about this awareness (high rates of unverbalized evaluation awareness); states that most automated ASL-4 evaluations for AI R&D and CBRN are saturated and no longer support a rule-out",
"source_url": "https://www.anthropic.com/claude-opus-4-6-system-card",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0854",
"date": "2026-02-07",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "OpenClaw",
"title": "OpenClaw adds automatic VirusTotal scanning for ClawHub skills",
"event": "Introduced automatic VirusTotal scanning of ClawHub skills — hash lookups + LLM-based behavioral analysis + daily rescans of active skills",
"significance": "An early case of bringing app-store-style pre-screening to the distribution of agent skills",
"source_url": "https://openclaw.ai/blog/virustotal-partnership",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0855",
"date": "2026-02-10",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Andrej Karpathy",
"title": "Andrej Karpathy proposes agentic engineering on X",
"event": "Proposed agentic engineering on X — wrote that the new default is not to write code directly 99% of the time but to orchestrate agents and act as a supervisor, and that the goal is to take the leverage of agents without compromising software quality at all",
"significance": "He himself puts forward a successor term to vibe coding, moving the discourse from giving up on quality to responsibility for supervision",
"source_url": "https://thenewstack.io/vibe-coding-is-passe/",
"source_type": "secondary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0856",
"date": "2026-02-11",
"date_precision": "day",
"axis": "5",
"axis_name": "Loop & Harness Engineering",
"also_axes": "",
"actor": "OpenAI (Ryan Lopopolo)",
"title": "Harness engineering",
"event": "Harness engineering: leveraging Codex in an agent-first world",
"significance": "An internal product of about 1 million lines written entirely by Codex; over 5 months, 3–7 engineers merged about 1,500 PRs",
"source_url": "https://openai.com/index/harness-engineering/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0857",
"date": "2026-02-12",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "Google DeepMind",
"title": "Google DeepMind upgrades Gemini 3 Deep Think",
"event": "Gemini 3 Deep Think upgrade — ARC-AGI-2 84.6%, HLE 48.4%",
"significance": "ARC-AGI-2 is effectively saturated. The announcement does not discuss AGI, framing the upgrade instead as accelerating science, research and engineering",
"source_url": "https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-3-deep-think/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0858",
"date": "2026-02-12",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "Anthropic",
"title": "Anthropic raises $30 billion Series G",
"event": "Series G — raised $30 billion at a $380 billion post-money valuation",
"significance": "The largest private funding round on record",
"source_url": "https://www.anthropic.com/news/anthropic-raises-30-billion-series-g-funding-380-billion-post-money-valuation",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0859",
"date": "2026-02-13",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "20",
"actor": "Dario Amodei (Dwarkesh)",
"title": "We are near the end of the exponential",
"event": "\"We are near the end of the exponential\" — at the same time, puts the probability of a country of geniuses in a datacenter within 10 years at 90%",
"significance": "A logical structure in which the end of scaling and the imminence of AGI are two sides of the same claim",
"source_url": "https://www.dwarkesh.com/p/dario-amodei-2",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0860",
"date": "2026-02-13",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "NAACP, SELC, Earthjustice",
"title": "NAACP, SELC and Earthjustice give notice of Clean Air Act suit against xAI",
"event": "Notice of intent to sue xAI under the Clean Air Act — 27 unpermitted gas turbines installed and operated in Southaven, Mississippi, up to 495 MW",
"significance": "Shows that the compute build-out proceeded in a way that bypassed environmental regulation. The setup avoided Memphis permitting by installing the turbines across the state line",
"source_url": "https://naacp.org/articles/naacp-selc-earthjustice-threaten-lawsuit-over-xais-unpermitted-gas-turbines-mississippi",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0861",
"date": "2026-02-14",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "",
"actor": "Peter Steinberger",
"title": "OpenClaw creator joins OpenAI, moves project to OpenClaw Foundation",
"event": "The creator announced he was joining OpenAI and transferred the project to the OpenClaw Foundation",
"significance": "The transition from a personal project to foundation governance happened at the same time as a hire by a frontier lab",
"source_url": "https://en.wikipedia.org/wiki/OpenClaw",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0862",
"date": "2026-02-17",
"date_precision": "day",
"axis": "5",
"axis_name": "Loop & Harness Engineering",
"also_axes": "",
"actor": "Birgitta Böckeler (martinfowler.com)",
"title": "Harness Engineering",
"event": "Harness Engineering — first thoughts",
"significance": "The first presentation of the guides/sensors × computational/inferential frame",
"source_url": "https://martinfowler.com/articles/exploring-gen-ai/harness-engineering-memo.html",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E1256",
"date": "2026-02-17",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "2",
"actor": "Anthropic",
"title": "Anthropic releases Claude Sonnet 4.6",
"event": "Claude Sonnet 4.6 released — 1M-token context window in beta, pricing unchanged at $3/$15 per 1M tokens",
"significance": "Became the default model for Free and Pro plans in claude.ai and Claude Cowork; Anthropic reported that early-access developers strongly preferred it to its predecessor and often to Opus 4.5 from November 2025",
"source_url": "https://www.anthropic.com/news/claude-sonnet-4-6",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E0863",
"date": "2026-02-18",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "Government of India",
"title": "India AI Impact Summit held; New Delhi Declaration adopted",
"event": "Hosted the India AI Impact Summit and adopted the New Delhi Declaration — endorsed by 92 countries and international organizations. All of it non-binding",
"significance": "The summit lineage moves outside the West",
"source_url": "https://www.pib.gov.in/PressReleasePage.aspx?PRID=2231208&v=4&reg=3&lang=2",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0864",
"date": "2026-02-19",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "Agility Robotics, Toyota Motor Manufacturing Canada",
"title": "Agility Robotics signs commercial RaaS deal with Toyota (2 Ontario plants)",
"event": "Commercial RaaS agreement (2 plants in Ontario)",
"significance": "A subscription-based commercial model for humanoids. The original announcement words the deployment as planned",
"source_url": "https://www.agilityrobotics.com/content/agility-robotics-announces-commercial-agreement-with-toyota-motor-manufacturing-canada",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0865",
"date": "2026-02-20",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "OpenAI",
"title": "OpenAI reportedly lowers 2030 compute spending target to about $600 billion",
"event": "Reported to have lowered its 2030 compute spending target to about $600 billion",
"significance": "The first official signal of a slowdown in the capital spending cycle",
"source_url": "https://www.cnbc.com/2026/02/20/openai-resets-spend-expectations-targets-around-600-billion-by-2030.html",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1257",
"date": "2026-02-20",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "Ministry of Science and ICT",
"title": "Korea adds Motif Technologies to its independent AI foundation model project",
"event": "Selected an additional elite team for the independent AI foundation model project — the Motif Technologies consortium (with Moreh, Crowdworks, NdotLight, KAIST and Mathpresso); Trillion Labs, the other applicant, narrowly missed out",
"significance": "Brought the project back to four teams after the first-round cut, all to be evaluated in August under the same development period, GPUs and data",
"source_url": "https://www.korea.kr/news/policyNewsView.do?newsId=156745273",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E0866",
"date": "2026-02-23",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "",
"actor": "Anthropic",
"title": "Anthropic accuses Chinese labs of distillation attacks",
"event": "Accused Chinese labs of distillation attacks — about 24,000 fraudulent accounts and more than 16 million conversations",
"significance": "The distillation dispute moves from suspicion to quantified accusation. Choosing to disclose its detection work and seek an industry-wide response rather than sue reflects the limits of legal remedies",
"source_url": "https://www.anthropic.com/news/detecting-and-preventing-distillation-attacks",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0867",
"date": "2026-02-23",
"date_precision": "day",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
"also_axes": "5",
"actor": "OpenAI Frontier Evals",
"title": "Why SWE-bench Verified no longer measures frontier coding capabilities",
"event": "Why SWE-bench Verified no longer measures frontier coding capabilities",
"significance": "At least 59.4% of the audited problems had flawed tests; the vendor acknowledges that the benchmark harness's signal is contaminated",
"source_url": "https://openai.com/index/why-we-no-longer-evaluate-swe-bench-verified/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0868",
"date": "2026-02-24",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "Anthropic",
"title": "Anthropic fully rewrites its RSP as v3.0",
"event": "Full rewrite as RSP v3.0 — introduces Risk Reports and Frontier Safety Roadmaps",
"significance": "v3.1 put in writing the freedom to halt development even when the RSP does not require it. The current version is v3.4",
"source_url": "https://www.anthropic.com/rsp-updates",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0869",
"date": "2026-02-24",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "METR",
"title": "METR announces a redesign of its developer productivity study",
"event": "Announced a change to the design of its developer productivity experiment",
"significance": "Redesigned the experiment because of selection bias (a growing number of participants refusing to work without AI). It did not retract its 2025 result of 19%, but said developers in early 2026 were likely faster, explicitly labeling this very weak evidence",
"source_url": "https://metr.org/blog/2026-02-24-uplift-update/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0870",
"date": "2026-02-25",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "National AI Strategy Committee (Korea)",
"title": "Korea announces its AI Action Plan (AI Basic Plan 2026–2028)",
"event": "Announced the Republic of Korea AI Action Plan (AI Basic Plan 2026–2028) — 3 pillars: building an AI innovation ecosystem, a nationwide AI-driven transformation, and contributing to a global AI basic society",
"significance": "Korea's first statutory AI basic plan. However, the version posted on the government portal has no GPU or budget figures; they appear only in the attached PDF",
"source_url": "https://www.aikorea.go.kr/web/board/brdDetail.do?currentPage=1&menu_cd=000011&num=328",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0871",
"date": "2026-03",
"date_precision": "month",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "Government of China",
"title": "China restricts OpenClaw use at state-owned enterprises and government agencies",
"event": "Restricted the use of OpenClaw at state-owned enterprises and government agencies, explicitly citing unauthorized data deletion, data leaks and excessive power consumption as the reasons",
"significance": "The first case of national-level restrictions on the use of an open-source personal agent",
"source_url": "https://en.wikipedia.org/wiki/OpenClaw",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0872",
"date": "2026-03-01",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "Samsung Electronics",
"title": "Samsung to turn its manufacturing sites into AI-driven autonomous factories by 2030",
"event": "Announced it will convert its manufacturing facilities in Korea and abroad into AI-driven autonomous factories by 2030 — digital twins of every process, 3 types of AI agents for quality, production and logistics, and phased deployment of humanoids",
"significance": "A directional signal from Korea's largest demand-side company. However, all of it is a 2030 target, and no figures on current deployment results are given",
"source_url": "https://news.samsung.com/kr/삼성전자-2030년까지-ai-자율공장-전환-추진",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0873",
"date": "2026-03-02",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "",
"actor": "US Supreme Court",
"title": "US Supreme Court declines to hear Thaler v. Perlmutter",
"event": "Denied certiorari in Thaler v. Perlmutter",
"significance": "The human authorship requirement for works generated by AI alone is settled",
"source_url": "https://www.mayerbrown.com/en/insights/publications/2026/03/supreme-court-denies-review-in-ai-authorship-case",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0874",
"date": "2026-03-03",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "UN",
"title": "UN scientific panel of 40 holds first meeting, elects Bengio and Ressa co-chairs",
"event": "The 40-member Independent International Scientific Panel held its first meeting and elected Bengio and Ressa as co-chairs",
"significance": "Selected from about 2,600 applicants from about 140 countries",
"source_url": "https://www.un.org/independent-international-scientific-panel-ai/en/faq",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0875",
"date": "2026-03-05",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Chen Yueh-Han, Robert McCarthy et al.",
"title": "Reasoning Models Struggle to Control their Chains of Thought published",
"event": "Published Reasoning Models Struggle to Control their Chains of Thought — CoT controllability of 2.7% versus output controllability of 61.9%",
"significance": "A follow-up that directly tested the premise of the Monitorability proposal to use CoT as a safety monitoring tool",
"source_url": "https://arxiv.org/abs/2603.05706",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0876",
"date": "2026-03-05",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "",
"actor": "Zadouri, Shah, Thakkar, Dao et al.",
"title": "FlashAttention-4",
"event": "FlashAttention-4 — co-design for asymmetric hardware scaling",
"significance": "The latest generation of the kernel lineage, formally published at MLSys 2026",
"source_url": "https://arxiv.org/abs/2603.05451",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0877",
"date": "2026-03-07",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "Saroj Mishra et al.",
"title": "Mishra et al. publish SoK: Agentic Retrieval-Augmented Generation",
"event": "Published SoK: Agentic Retrieval-Augmented Generation — formalizes the retrieval loop as a Markov decision process and catalogs vulnerabilities such as memory poisoning and retrieval misalignment",
"significance": "The first systematic taxonomy of agentic RAG. A reference point for follow-up research",
"source_url": "https://arxiv.org/abs/2603.07379",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0878",
"date": "2026-03-09",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "Yann LeCun / AMI Labs",
"title": "AMI Labs raises $1.03 billion at a $3.5 billion pre-money valuation",
"event": "Raised $1.03 billion at a pre-money valuation of $3.5 billion",
"significance": "LLM skepticism capitalized as a $1 billion-scale bet on an alternative route",
"source_url": "https://techcrunch.com/2026/03/09/yann-lecuns-ami-labs-raises-1-03-billion-to-build-world-models/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0879",
"date": "2026-03-11",
"date_precision": "day",
"axis": "19",
"axis_name": "On-Device & Local Inference",
"also_axes": "",
"actor": "llama.cpp (ggml-org)",
"title": "llama.cpp merges PR #19769, adding NVFP4",
"event": "Merged PR #19769 — introduced GGML_TYPE_NVFP4. FP4 E2M1 weights + UE4M3 block scales, loading NVIDIA ModelOpt outputs directly",
"significance": "GGUF absorbed a hardware-native FP4 format beyond its own K-quants",
"source_url": "https://github.com/ggml-org/llama.cpp/pull/19769",
"source_type": "primary",
"claim_type": "community",
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{
"id": "E0880",
"date": "2026-03-12",
"date_precision": "day",
"axis": "5",
"axis_name": "Loop & Harness Engineering",
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"actor": "HumanLayer",
"title": "Skill Issue: Harness Engineering for Coding Agents",
"event": "Skill Issue: Harness Engineering for Coding Agents",
"significance": "Defined harness engineering as a subset of context engineering, with the concepts of a context firewall and back-pressure",
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"id": "E0881",
"date": "2026-03-12",
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"axis_name": "Embeddings & Vector Search",
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"actor": "Qdrant",
"title": "Qdrant announces a $50 million Series B",
"event": "Announced a $50 million Series B — put forward the concept of composable vector search (combining dense and sparse vectors, metadata filters, multivectors and custom scoring at query time). 250 million cumulative downloads",
"significance": "The counterargument of the dedicated vector DB camp. A direct reply to integrated options (Postgres, Elasticsearch)",
"source_url": "https://qdrant.tech/blog/series-b-announcement/",
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{
"id": "E0882",
"date": "2026-03-16",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
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"actor": "Mistral AI",
"title": "Mistral AI releases Leanstral",
"event": "Released Leanstral — an open-source code agent for Lean 4 (120B-A6B, Apache 2.0). Released FLTEval, a formal-methods evaluation suite, at the same time",
"significance": "A move toward mechanically checking AI-generated code against formal specifications. Shifted the evaluation axis from competition math to contributions to real formalization projects",
"source_url": "https://mistral.ai/news/leanstral/",
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{
"id": "E0883",
"date": "2026-03-16",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
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"actor": "Mistral AI",
"title": "Mistral AI releases Mistral Small 4",
"event": "Released Mistral Small 4 — 119B total / 6B active MoE, 256k context, Apache 2.0. Merges Magistral (reasoning), Devstral (coding) and Small (instruct) into one, with reasoning effort adjustable as a parameter",
"significance": "Brought the trend of merging separate model families into a single set of weights to open weights",
"source_url": "https://mistral.ai/news/mistral-small-4/",
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{
"id": "E0884",
"date": "2026-03-19",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Shiyan Liu et al.",
"title": "Liu et al. publish Reflection in the Dark",
"event": "Published Reflection in the Dark — demonstrated conditions under which reflective prompt optimization worsens accuracy from 23.81% to 13.50%",
"significance": "The first to isolate a failure mode of GEPA-style automatic prompt optimization",
"source_url": "https://arxiv.org/abs/2603.18388",
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{
"id": "E0885",
"date": "2026-03-21",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
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"actor": "Hugging Face",
"title": "Hugging Face archives the text-generation-inference repository",
"event": "Archived the text-generation-inference repository",
"significance": "The README recommends moving to vLLM, SGLang, llama.cpp or MLX; the serving engine market consolidated into two leaders plus local",
"source_url": "https://github.com/huggingface/text-generation-inference",
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{
"id": "E0886",
"date": "2026-03-23",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
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"actor": "Cisco",
"title": "Cisco releases DefenseClaw",
"event": "Released DefenseClaw — an open-source governance layer that sits on top of the NVIDIA OpenShell sandbox (pre-execution scanning, runtime inspection, instant policy revocation). Cited more than 135,000 exposed instances as the background",
"significance": "The agent security stack split into layers: infrastructure sandbox and operational governance",
"source_url": "https://blogs.cisco.com/ai/cisco-announces-defenseclaw",
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{
"id": "E0887",
"date": "2026-03-24",
"date_precision": "day",
"axis": "5",
"axis_name": "Loop & Harness Engineering",
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"actor": "Anthropic Labs (Prithvi Rajasekaran)",
"title": "Harness design for long-running application development",
"event": "Harness design for long-running application development",
"significance": "3 agents — Planner, Generator, Evaluator; a single agent ran 20 minutes for $9 versus 6 hours for $200 with the full harness",
"source_url": "https://www.anthropic.com/engineering/harness-design-long-running-apps",
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{
"id": "E0888",
"date": "2026-03-24",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "",
"actor": "vLLM",
"title": "vLLM introduces Model Runner V2",
"event": "Model Runner V2 — a modular execution core with GPU-native input preparation and asynchronous scheduling",
"significance": "The second rewrite of the execution layer since the V1 redesign. Serving competition moved from kernels to schedulers and execution cores",
"source_url": "https://vllm.ai/blog",
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{
"id": "E0889",
"date": "2026-03-25",
"date_precision": "day",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
"also_axes": "6",
"actor": "ARC Prize",
"title": "ARC Prize releases ARC-AGI-3",
"event": "Released ARC-AGI-3 — humans 100% versus frontier AI 0.51%",
"significance": "The axis moved from static problem solving to interactive exploration and skill acquisition without instructions",
"source_url": "https://arcprize.org/blog/arc-agi-3-launch",
"source_type": "primary",
"claim_type": "fact",
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},
{
"id": "E0890",
"date": "2026-03-26",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "Sakana AI",
"title": "Sakana AI's AI Scientist research is published in Nature",
"event": "Its AI Scientist research was published in Nature — reported that a paper that passed review at an ICLR 2025 workshop had an average score of 6.33, above 55% of human-written papers",
"significance": "AI-generated papers passing review entered the formal scholarly record. The debate over how to evaluate automated research got going in earnest",
"source_url": "https://sakana.ai/ai-scientist-nature-jp/",
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{
"id": "E0891",
"date": "2026-03-26",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Cohere",
"title": "Cohere releases Cohere Transcribe",
"event": "Released Cohere Transcribe — an open-source ASR model with 2B parameters, Apache 2.0, 14 languages",
"significance": "A lab centered on text LLMs released speech recognition under a fully open license",
"source_url": "https://cohere.com/blog/transcribe",
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{
"id": "E0892",
"date": "2026-03-27",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
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"actor": "Chaoyang University of Technology (Chayanon Sub-r-pa, Rung-Ching Chen)",
"title": "YOLO-World v2 applied to MVTec AD with an extreme 1:9 train-validation split",
"event": "Applied YOLO-World v2 to MVTec AD with an extreme 1:9 train-validation split — an average of 11.8 defect instances per category. By the authors' measurement, it beat YOLOv11s and YOLOv26s in 12 of 15 categories; the weakest category, with 9 training instances, reached 64.9% mAP@50",
"significance": "An experimental design that fits the OSAT reality, where a new defect class has to start from 10 images or fewer. However, the absolute performance is at the level of inspection support and triage, not production pass/fail decisions",
"source_url": "https://www.mdpi.com/1999-4893/19/4/259",
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{
"id": "E0893",
"date": "2026-03-31",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "",
"actor": "Google Cloud",
"title": "Google Cloud makes TPU7x generally available (GA)",
"event": "TPU7x generally available (GA) — 192GB HBM per chip, 7.37TB/s",
"significance": "Ironwood went from announcement to actual supply",
"source_url": "https://docs.cloud.google.com/tpu/docs/tpu7x",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
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{
"id": "E0894",
"date": "2026-04",
"date_precision": "month",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "François Chollet",
"title": "François Chollet shortens his AGI timeline from 10 years to about 5",
"event": "Shortened his AGI timeline from 10 years to about 5",
"significance": "A conditional shift, citing the emergence of fluid intelligence that adapts to novelty at test time",
"source_url": "https://www.eweek.com/news/chollet%E2%80%91cuts%E2%80%91agi%E2%80%91timeline%E2%80%91to%E2%80%915%E2%80%91years%E2%80%91with%E2%80%91new%E2%80%91benchmark/",
"source_type": "secondary",
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{
"id": "E0895",
"date": "2026-04",
"date_precision": "month",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "IEA",
"title": "IEA: data center power use up 17% in 2025; AI-specific demand to triple by 2030",
"event": "Data center electricity demand grew 17% in 2025, and AI-specific demand is projected to triple by 2030",
"significance": "Officially acknowledged that power and grid connection delays are the physical bottleneck limiting the speed of near-term expansion",
"source_url": "https://www.iea.org/news/data-centre-electricity-use-surged-in-2025-even-with-tightening-bottlenecks-driving-a-scramble-for-solutions",
"source_type": "primary",
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{
"id": "E0896",
"date": "2026-04-01",
"date_precision": "day",
"axis": "21",
"axis_name": "Graph Engineering",
"also_axes": "2;16",
"actor": "Dongzhe Fan et al.",
"title": "Fan et al. publish Do We Still Need GraphRAG?",
"event": "Published Do We Still Need GraphRAG? — used the RAGSearch benchmark to compare dense RAG and GraphRAG, each combined with agentic search",
"significance": "Showed that agentic search narrows the gap between the two approaches, updating the 2024 GraphRAG debate",
"source_url": "https://arxiv.org/abs/2604.09666",
"source_type": "primary",
"claim_type": "fact",
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},
{
"id": "E0897",
"date": "2026-04-02",
"date_precision": "day",
"axis": "4",
"axis_name": "MCP & Skills Standardization",
"also_axes": "",
"actor": "AAIF, Linux Foundation",
"title": "MCP Dev Summit North America 2026 (New York, 1,200 attendees)",
"event": "MCP Dev Summit North America 2026 (New York, 1,200 attendees)",
"significance": "AAIF reached 170 member companies in 4 months, MCP SDK monthly downloads passed 110 million, and security — including a DNS rebinding demo — was the biggest topic",
"source_url": "https://aaif.io/press/agentic-ai-foundation-unveils-mcp-dev-summit-north-america-2026-schedule/",
"source_type": "primary",
"claim_type": "fact",
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{
"id": "E0898",
"date": "2026-04-02",
"date_precision": "day",
"axis": "5",
"axis_name": "Loop & Harness Engineering",
"also_axes": "",
"actor": "Birgitta Böckeler (martinfowler.com)",
"title": "Harness engineering for coding agent users",
"event": "Harness engineering for coding agent users — Agent = Model + Harness",
"significance": "The standard reference for the user-side definition of the harness as everything in an AI agent except the model",
"source_url": "https://martinfowler.com/articles/harness-engineering.html",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0899",
"date": "2026-04-02",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Google",
"title": "Gemma 4",
"event": "Gemma 4 — the first in the Gemmaverse released under the OSI-approved Apache 2.0",
"significance": "The opening signal of the US camp's return to truly open releases",
"source_url": "https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/",
"source_type": "primary",
"claim_type": "fact",
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{
"id": "E0900",
"date": "2026-04-02",
"date_precision": "day",
"axis": "19",
"axis_name": "On-Device & Local Inference",
"also_axes": "9",
"actor": "Google (Android)",
"title": "Google releases Gemma 4 as an AICore developer preview",
"event": "Released Gemma 4 as an AICore developer preview — E4B/E2B, more than 140 languages, multimodal; claims up to 4× faster than the previous generation and up to 60% battery savings (preview, not GA)",
"significance": "On-device AI on Android evolved into a system in which a system service (AICore) distributes open models directly",
"source_url": "https://android-developers.googleblog.com/2026/04/AI-Core-Developer-Preview.html",
"source_type": "primary",
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},
{
"id": "E0901",
"date": "2026-04-03",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "Krishnamoorthy et al. (multi-institution: India, Malaysia, Iraq)",
"title": "Self-supervised multi-resolution learning of thin-film SEM defect morphology",
"event": "Learned representations of thin-film SEM defect morphology with self-supervised multi-resolution learning — no defect labels used in training at all. By the authors' measurement it had the best silhouette score, 0.50, but its NMI, which measures agreement with the actual classes, was 0.203, lower than classic GLCM texture features (0.21–0.29)",
"significance": "Grounds for a workflow that clusters defect morphologies with zero labels and picks only representative samples for labelling. At the same time, a counterexample showing that internal clustering metrics and agreement with the actual classes can diverge",
"source_url": "https://www.nature.com/articles/s41598-026-46947-3",
"source_type": "primary",
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{
"id": "E0902",
"date": "2026-04-06",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
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"actor": "Anthropic, Google, Broadcom",
"title": "Anthropic signs multi-GW next-gen TPU deal, discloses run-rate revenue over $30B",
"event": "A multi-GW deal for next-generation TPUs; disclosed run-rate revenue of more than $30 billion",
"significance": "The full-scale expansion of the alternative silicon camp",
"source_url": "https://www.anthropic.com/news/google-broadcom-partnership-compute",
"source_type": "primary",
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"added_week": "2026-W35"
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{
"id": "E0903",
"date": "2026-04-08",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
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"actor": "Meta Superintelligence Labs",
"title": "Meta Superintelligence Labs releases Muse Spark",
"event": "Released Muse Spark — native multimodal reasoning, tool use, visual CoT and multi-agent orchestration. Offered only on meta.ai and as a private API preview; weights not released",
"significance": "The point at which Meta's two-track strategy was settled: the frontier model closed, with only separate small models open",
"source_url": "https://ai.meta.com/blog/introducing-muse-spark-msl/",
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{
"id": "E0904",
"date": "2026-04-08",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Meta",
"title": "Meta announces Muse Spark",
"event": "Announced Muse Spark — the first MSL model, in private preview for selected partners",
"significance": "The replacement of the Llama brand and the shift to closed",
"source_url": "https://about.fb.com/news/2026/04/introducing-muse-spark-meta-superintelligence-labs/",
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{
"id": "E0905",
"date": "2026-04-09",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
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"actor": "LG AI Research",
"title": "LG AI Research releases EXAONE 4.5 33B",
"event": "Released EXAONE 4.5 33B — LG's first open-weight vision-language model",
"significance": "As LG's first open-weight vision-language model, it marks the point where Korea's open-weight route expanded beyond text",
"source_url": "https://www.lgresearch.ai/blog/view?seq=640",
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{
"id": "E0906",
"date": "2026-04-09",
"date_precision": "day",
"axis": "19",
"axis_name": "On-Device & Local Inference",
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"actor": "Microsoft",
"title": "Microsoft makes Foundry Local generally available (GA)",
"event": "Foundry Local generally available (GA) — a cross-platform on-device inference stack for Windows, Linux and macOS, with automatic GPU, NPU and CPU acceleration and native Metal on Apple Silicon",
"significance": "Made local inference, free of cloud lock-in and per-token billing, an official product line",
"source_url": "https://devblogs.microsoft.com/foundry/foundry-local-ga/",
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{
"id": "E0907",
"date": "2026-04-10",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
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"actor": "Sam Altman",
"title": "Sam Altman blogs on AI power concentration and the OpenAI board conflict",
"event": "Posted on his blog warning against the concentration of AI power (no one should have the ring) and reflecting on his own part in the OpenAI board conflict",
"significance": "A rare record of a lab head addressing his own company's governance problems in his own voice",
"source_url": "https://blog.samaltman.com/2279512",
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{
"id": "E0908",
"date": "2026-04-13",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
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"actor": "Stanford HAI",
"title": "AI Index 2026",
"event": "AI Index 2026 — added a new Science chapter",
"significance": "AI for science became a subject of measurement in its own right",
"source_url": "https://hai.stanford.edu/news/inside-the-ai-index-12-takeaways-from-the-2026-report",
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{
"id": "E0909",
"date": "2026-04-16",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "",
"actor": "Anthropic",
"title": "Claude Opus 4.7",
"event": "Claude Opus 4.7 — high-resolution vision up to 2,576px on the long edge",
"significance": "Showed that reading dense screenshots and diagrams had been the bottleneck in computer use performance",
"source_url": "https://www.anthropic.com/news/claude-opus-4-7",
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{
"id": "E0910",
"date": "2026-04-16",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "Physical Intelligence",
"title": "π0.7",
"event": "π0.7 — multimodal prompting",
"significance": "Succeeded at folding laundry on a bimanual robot with no training data at all — cross-embodiment transfer",
"source_url": "https://www.pi.website/blog/pi07",
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{
"id": "E0911",
"date": "2026-04-17",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "",
"actor": "Alibaba Qwen",
"title": "Alibaba Qwen publishes the Qwen3.5-Omni technical report",
"event": "Qwen3.5-Omni technical report — Thinker-Talker Hybrid-Attention MoE, 256k context",
"significance": "Open-weight omni-modal models caught up with the frontier, handling 10 hours of audio and 400 seconds of video",
"source_url": "https://arxiv.org/abs/2604.15804",
"source_type": "primary",
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{
"id": "E0912",
"date": "2026-04-19",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "Jingbo Sun et al.",
"title": "Sun et al. present AutoSearch",
"event": "Presented AutoSearch — uses reinforcement learning to decide the minimum number of search steps based on question difficulty",
"significance": "A successor in the Search-R1 line. Made the number of searches itself something to learn",
"source_url": "https://arxiv.org/abs/2604.17337",
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{
"id": "E0913",
"date": "2026-04-20",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "16",
"actor": "David C. Krakauer",
"title": "The Rise and Fall of G in AGI",
"event": "The Rise and Fall of G in AGI",
"significance": "The variance explained by the first principal component across benchmarks fell from 92% in 2023–24 to 64% after reasoning models arrived — an argument for specialization beneath apparent generality",
"source_url": "https://arxiv.org/html/2604.09911",
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{
"id": "E0914",
"date": "2026-04-21",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
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"actor": "Moonshot AI",
"title": "Moonshot AI releases Kimi K2.6",
"event": "Released Kimi K2.6 — 1T total / 32B active, up to 13 hours of continuous coding, 300 subagents collaborating across 4,000 steps. SWE-Bench Verified 80.2% (self-reported)",
"significance": "An open-weight model entered the 80% range on SWE-Bench Verified. No independent party checking the result is named",
"source_url": "https://www.yicaiglobal.com/news/chinas-moonshot-ai-releases-kimi-k26-pushing-boundaries-in-coding-multi-agent-capabilities",
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{
"id": "E0915",
"date": "2026-04-22",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "",
"actor": "Google Cloud (Cloud Next)",
"title": "Google Cloud previews its 8th-generation TPUs at Cloud Next",
"event": "Previewed its 8th-generation TPUs — split into TPU 8t (training) and TPU 8i (inference)",
"significance": "Split training and inference silicon for the first time; a signal that the era of general-purpose accelerators is ending",
"source_url": "https://www.storagereview.com/news/google-announces-tpu-8t-sunfish-and-tpu-8i-zebrafish",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0916",
"date": "2026-04-22",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Alibaba",
"title": "Alibaba releases Qwen3.6-27B under Apache 2.0",
"event": "Released Qwen3.6-27B under Apache 2.0 — SWE-bench Verified 77.2%",
"significance": "A dense 27B beat its 397B MoE predecessor, and the center of gravity of truly open models moved down to the 30B class",
"source_url": "https://www.remio.ai/post/qwen3-6-open-source-model-beats-a-397b-giant-while-alibaba-quietly-closes-weights-on-its-flagshi",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0917",
"date": "2026-04-22",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "Hexagon, Schaeffler",
"title": "Hexagon and Schaeffler agree to deploy at least 1,000 AEON humanoids by 2032",
"event": "Agreed to deploy at least 1,000 AEON humanoids across a global production network by 2032",
"significance": "A large-scale commitment from a components and bearings manufacturer (planned figure)",
"source_url": "https://hexagon.com/company/newsroom/press-releases/2026/hexagon-schaeffler-deploy-aeon-humanoids-across-global-factory-network",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0918",
"date": "2026-04-22",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "7",
"actor": "Google",
"title": "Google makes Gemini Embedding 2 generally available (GA)",
"event": "Gemini Embedding 2 generally available (GA) — a natively multimodal embedding that supports search across text, images, video and audio",
"significance": "The commercial embedding lineage officially moved past the text-only generation into a natively multimodal one",
"source_url": "https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-embedding-2-generally-available/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0919",
"date": "2026-04-23",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "",
"actor": "OpenAI",
"title": "GPT-5.5",
"event": "GPT-5.5 — OSWorld-Verified 78.7%, MMMU-Pro 81.2%",
"significance": "Showed in numbers that visual understanding and operating a computer are the same capability",
"source_url": "https://openai.com/index/introducing-gpt-5-5/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0920",
"date": "2026-04-24",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "DeepSeek",
"title": "DeepSeek releases V4-Pro (1.6T/49B) and V4-Flash (284B/13B), MIT, 1M context",
"event": "DeepSeek V4-Pro (1.6T/49B) and V4-Flash (284B/13B), MIT, 1M context",
"significance": "Re-entered the frontier while keeping MIT",
"source_url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0921",
"date": "2026-04-24",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "Cohere",
"title": "Cohere announces acquisition of and merger with Germany's Aleph Alpha",
"event": "Announced the acquisition of and merger with Germany's Aleph Alpha — the merged company is valued at $20 billion",
"significance": "Consolidation of the North American and European sovereign AI camps. The end of independence for Aleph Alpha, once Europe's flagship lab",
"source_url": "https://techcrunch.com/2026/04/24/cohere-acquires-merges-with-german-based-startup-to-create-a-transatlantic-ai-powerhouse/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0922",
"date": "2026-04-26",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "",
"actor": "DeepSeek-AI",
"title": "DeepSeek-V4",
"event": "DeepSeek-V4 — CSA and HCA hybrid attention, mHC, Muon, 32T+ tokens",
"significance": "At 27% of the per-token FLOPs and 10% of the KV cache of V3.2, it made 1M context something supported all the time",
"source_url": "https://arxiv.org/abs/2606.19348",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0923",
"date": "2026-04-27",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "OpenAI, Microsoft",
"title": "The AGI clause between OpenAI and Microsoft effectively dies",
"event": "The AGI clause effectively ended — revenue sharing continues regardless of technical progress",
"significance": "An event that revealed the definition of AGI had really been a contractual and governance device",
"source_url": "https://simonwillison.net/2026/Apr/27/now-deceased-agi-clause/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0924",
"date": "2026-05-05",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "Anthropic Fellows",
"title": "Model Spec Midtraining",
"event": "Model Spec Midtraining",
"significance": "Putting a norms document into midtraining, before alignment fine-tuning, improves generalization",
"source_url": "https://alignment.anthropic.com/2026/msm/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0925",
"date": "2026-05-05",
"date_precision": "day",
"axis": "19",
"axis_name": "On-Device & Local Inference",
"also_axes": "4",
"actor": "Google (Chrome)",
"title": "Chrome 148 stable opens the Prompt API to the web",
"event": "Opened the Prompt API to the web in Chrome 148 stable — for extensions it has been stable since Chrome 138. Web pages access the browser's built-in Gemini Nano directly, with support for text, image and audio input and for constraining responses with regular expressions and JSON schemas. Requirements: 22GB of free space, and more than 4GB of VRAM or 16GB of RAM and 4 or more cores",
"significance": "A stable-channel case of an on-device model exposed as a web platform API rather than an app. Responsibility for model distribution and storage management passes to the browser vendor",
"source_url": "https://developer.chrome.com/release-notes/148",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W36"
},
{
"id": "E0926",
"date": "2026-05-06",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "xAI (SpaceXAI)",
"title": "xAI signs a deal giving Anthropic access to Colossus 1",
"event": "Signed a deal giving Anthropic access to Colossus 1. Colossus 1 has more than 220,000 NVIDIA GPUs",
"significance": "A structural shift to renting compute to a rival lab. Also the latest primary source for the number of GPUs actually running in Colossus 1. Amount and term not disclosed",
"source_url": "https://x.ai/news/anthropic-compute-partnership",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0927",
"date": "2026-05-06",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "Earthjustice",
"title": "Earthjustice seeks preliminary injunction against xAI and subsidiary MZX Tech",
"event": "Filed for a preliminary injunction against xAI and its subsidiary MZX Tech. The turbines had been expanded from 27 to 33",
"significance": "Legal proceedings established that, 3 months after notice of intent to sue, the number of turbines had actually gone up",
"source_url": "https://earthjustice.org/press/2026/naacp-asks-court-for-emergency-action-to-stop-illegal-air-pollution-from-xais-data-center-power-plant",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0928",
"date": "2026-05-06",
"date_precision": "day",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
"also_axes": "",
"actor": "Laude Institute",
"title": "Laude Institute releases Terminal-Bench 2.1",
"event": "Released Terminal-Bench 2.1 — fixed 28 tasks because of changes in external dependencies, resource mismatches and specification errors",
"significance": "A case of a benchmark publicly admitting its own defects and introducing an ongoing checking process",
"source_url": "https://www.tbench.ai/news/terminal-bench-2-1",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0929",
"date": "2026-05-06",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "5",
"actor": "Simon Willison",
"title": "Simon Willison writes vibe coding and agentic engineering are getting closer",
"event": "Posted Vibe coding and agentic engineering are getting closer than I'd like on his blog — confessed that as coding agents become trustworthy, even he no longer reviews code line by line, so the boundary between the two approaches is blurring",
"significance": "The key chronicler of vibe coding's spread declared that the very distinction between the two axes of 2026 coding discourse is breaking down",
"source_url": "https://simonwillison.net/2026/May/6/vibe-coding-and-agentic-engineering/",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0930",
"date": "2026-05-07",
"date_precision": "day",
"axis": "5",
"axis_name": "Loop & Harness Engineering",
"also_axes": "",
"actor": "Zhang et al. (arXiv:2605.23950)",
"title": "Stop Comparing LLM Agents Without Disclosing the Harness",
"event": "Stop Comparing LLM Agents Without Disclosing the Harness",
"significance": "Harness-induced variance was 7.80× model-induced variance, and rankings flipped in 6 of 9 model pairs",
"source_url": "https://arxiv.org/abs/2605.23950",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0931",
"date": "2026-05-07",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "",
"actor": "OpenAI",
"title": "GPT-Realtime-2 / -Translate / -Whisper",
"event": "GPT-Realtime-2 / -Translate / -Whisper",
"significance": "Brought reasoning effort and parallel tool calls to voice models and raised context from 32K to 128K. Voice became an environment for running agents",
"source_url": "https://openai.com/index/advancing-voice-intelligence-with-new-models-in-the-api/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0932",
"date": "2026-05-07",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "Moonshot AI",
"title": "Moonshot AI raises $2 billion at a $20 billion valuation",
"event": "Raised $2 billion at a $20 billion valuation. ARR passed $200 million in April",
"significance": "Raised $3.9 billion over 6 months. A case showing that open-weight distribution works commercially",
"source_url": "https://techcrunch.com/2026/05/07/chinas-moonshot-ai-raises-2b-at-20b-valuation-as-demand-for-open-source-ai-skyrockets/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0933",
"date": "2026-05-07",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "Google DeepMind",
"title": "Google DeepMind reports AlphaEvolve's first-year results",
"event": "AlphaEvolve's results after 1 year — 30% fewer variant detection errors, AC-OPF feasible solutions up from 14% to more than 88%",
"significance": "Real progress in domains that can be checked automatically (Google's own measurement)",
"source_url": "https://deepmind.google/blog/alphaevolve-impact/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0934",
"date": "2026-05-07",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "Council of the EU, European Parliament",
"title": "EU Council and Parliament provisionally agree on the Digital Omnibus (AI)",
"event": "Provisional agreement on the Digital Omnibus (AI) — the date of political agreement. Postponed the application dates for high-risk systems to Dec 2, 2027 (stand-alone) and Aug 2, 2028 (embedded in products)",
"significance": "The moment the effective postponement of the AI Act's high-risk obligations was decided",
"source_url": "https://www.consilium.europa.eu/en/press/press-releases/2026/05/07/artificial-intelligence-council-and-parliament-agree-to-simplify-and-streamline-rules/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0935",
"date": "2026-05-08",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Naoto Iwase et al.",
"title": "Iwase et al. publish Reliable Chain-of-Thought via Prefix Consistency",
"event": "Published Reliable Chain-of-Thought via Prefix Consistency — matched self-consistency's accuracy with a median of 4.6× fewer tokens",
"significance": "A follow-up that rebuilt the cost structure of 2022's self-consistency",
"source_url": "https://arxiv.org/abs/2605.07654",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0936",
"date": "2026-05-08",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "Anthropic",
"title": "Teaching Claude Why",
"event": "Teaching Claude Why",
"significance": "Training on reasons rather than behaviors cut the blackmail rate from 65% to 19%, and out-of-distribution data was about 28× more efficient than honeypots",
"source_url": "https://alignment.anthropic.com/2026/teaching-claude-why/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0937",
"date": "2026-05-11",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "METR",
"title": "Measuring the Self-Reported Impact of Early-2026 AI on Technical Worker Productivity",
"event": "Published Measuring the Self-Reported Impact of Early-2026 AI on Technical Worker Productivity — a self-report survey of 349 technical workers, with a median change of 1.4–2× by value. The authors also gave reasons to doubt those figures",
"significance": "The first follow-up result of the methodology overhaul announced on Feb 24, 2026. It noted again that, in the 2025 study, respondents had overestimated the impact on time taken by 40 percentage points on average",
"source_url": "https://metr.org/blog/2026-05-11-ai-usage-survey/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0938",
"date": "2026-05-11",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "Shanghai Huahong Grace Semiconductor (上海華虹宏力), East China University of Science and Technology",
"title": "Hua Hong Grace and ECUST present WaferSAGE",
"event": "Presented WaferSAGE — wafer defect VQA with a 4B-parameter VLM. On the authors' LLM-judge measurement, the 4B model scored 6.493, recovering about 90% of Gemini-3-Flash's 7.149 and far exceeding the 106B GLM-4.6V (4.750). However, GSPO reinforcement learning contributed only +0.009 over SFT",
"significance": "The authors include an actual semiconductor manufacturer, and the design explicitly assumes on-premises use with no data leaving the site. It means defect report automation is possible with 1–2 in-house GPUs. There is, however, no case of mass-production deployment",
"source_url": "https://arxiv.org/html/2604.27629",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0939",
"date": "2026-05-12",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "10",
"actor": "Sam Martin, Fabien Roger",
"title": "Martin and Roger publish Classifier Context Rot",
"event": "Published Classifier Context Rot — frontier models missed dangerous behavior that came after 800,000 tokens of harmless activity up to 30× more often",
"significance": "Empirical evidence that long-context limits also break down safety monitoring performance",
"source_url": "https://arxiv.org/abs/2605.12366",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0940",
"date": "2026-05-14",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "Governor of Colorado",
"title": "Colorado governor signs SB 26-189",
"event": "Signed SB 26-189 — repeals and reenacts SB 24-205, effective Jan 1, 2027. Removes the duty of care against discrimination and replaces it with a focus on transparency in automated decision-making and human review",
"significance": "A de facto retreat from the first comprehensive state AI law in the US. An event you must check before citing Colorado",
"source_url": "https://leg.colorado.gov/bills/sb26-189",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0941",
"date": "2026-05-15",
"date_precision": "day",
"axis": "5",
"axis_name": "Loop & Harness Engineering",
"also_axes": "",
"actor": "Addy Osmani (O'Reilly Radar)",
"title": "Agent Harness Engineering",
"event": "Agent Harness Engineering",
"significance": "The containment position: the harness is the delivery vehicle of context engineering",
"source_url": "https://www.oreilly.com/radar/agent-harness-engineering/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0942",
"date": "2026-05-15",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "OpenClaw (Jesse Merhi)",
"title": "OpenClaw publishes its security roadmap",
"event": "Published its security roadmap — fs-safe path escape prevention, Proxyline SSRF defenses, ClawHub trust signals, 148 OpenGrep rules. At the same time stated explicitly that this is not a sandbox and that native modules and raw sockets can get around it",
"significance": "A public admission by the maintainers that agent runtimes cannot be defended with language-level guardrails",
"source_url": "https://openclaw.ai/blog/where-openclaw-security-is-heading",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0943",
"date": "2026-05-19",
"date_precision": "day",
"axis": "19",
"axis_name": "On-Device & Local Inference",
"also_axes": "8",
"actor": "Google (AI Edge)",
"title": "Google makes LiteRT-LM generally available (GA)",
"event": "LiteRT-LM generally available (GA) — added a native Swift API for iOS and a WebGPU-accelerated JavaScript API. Supports Android CPU, OpenCL GPU and NPU, iOS Metal and browser WebGPU. With Gemma 4: 52 tok/s decode on Android GPU, 56 tok/s on iOS GPU, 76 tok/s on the web on a MacBook Pro M4 Max, up to 2.2× with Multi-Token Prediction, 607MB of memory on iOS",
"significance": "The stage at which an on-device runtime covers not just mobile OSes but browsers with the same stack. Figures are the vendor's own measurements",
"source_url": "https://developers.googleblog.com/blazing-fast-on-device-genai-with-litert-lm/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W36"
},
{
"id": "E0944",
"date": "2026-05-19",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Andrej Karpathy",
"title": "Andrej Karpathy announces he is joining Anthropic's pretraining team",
"event": "Announced he is joining Anthropic's pretraining team — under Nick Joseph",
"significance": "The most-cited person in these records moved inside a frontier lab. The nature of his public writing may change from here on",
"source_url": "https://techcrunch.com/2026/05/19/openai-co-founder-andrej-karpathy-joins-anthropics-pre-training-team/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0945",
"date": "2026-05-20",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Cohere",
"title": "Cohere releases Command A+",
"event": "Released Command A+ — 218B total / 25B active MoE, Apache 2.0. Minimum hardware: 1 B200 or 2 H100s (W4A4)",
"significance": "Released a 200B-class MoE under Apache 2.0 while lowering the bar for on-premises deployment to 1–2 GPUs",
"source_url": "https://cohere.com/blog/command-a-plus",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0946",
"date": "2026-05-20",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "Hyundai Motor Group",
"title": "Hyundai Motor Group announces plans to deploy more than 25,000 Atlas robots",
"event": "Announced plans to deploy more than 25,000 Atlas units",
"significance": "The largest planned figure from a single company. Metaplant America in 2028, Kia Georgia in 2029",
"source_url": "https://www.koreaherald.com/article/10741955",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0947",
"date": "2026-05-21",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "Ministry of Science and ICT",
"title": "Science ministry pre-announces amendments to the AI Basic Act enforcement decree",
"event": "Pre-announced the draft amendment to the AI Basic Act enforcement decree — this is the pre-announcement date; effective Jul 21, 2026. Specifies 11 categories of AI-vulnerable groups and priority consideration in public procurement",
"significance": "This is the pre-announcement stage, not the final decree. It shows the draft form of the vulnerable-group categories and the public procurement provisions",
"source_url": "https://www.lawtimes.co.kr/news/articleView.html?idxno=222514",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0948",
"date": "2026-05-22",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Jinghan Jia, Joe Benton, Eric Easley (Anthropic)",
"title": "Anthropic researchers publish Faithfulness as Information Flow",
"event": "Published Faithfulness as Information Flow — diagnoses CoT faithfulness by sufficiency, completeness and necessity, and proposes training interventions such as attention masking",
"significance": "The first follow-up since Anthropic's April 2025 disclosure of unfaithfulness to propose interventions at the training stage",
"source_url": "https://arxiv.org/abs/2605.24286",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0949",
"date": "2026-05-24",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Jingyi Sun et al. (University of Copenhagen and others)",
"title": "Contextual and parametric faithfulness shown to correlate only asymmetrically",
"event": "Demonstrated that contextual faithfulness and parametric faithfulness are correlated only asymmetrically — FaithMATE",
"significance": "Showed that faithfulness is not a single goal but several axes pulling against each other",
"source_url": "https://arxiv.org/abs/2605.24960",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0950",
"date": "2026-05-27",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Guni Sharon",
"title": "Guni Sharon publishes Tree of Thoughts as a Classical Heuristic Search Problem",
"event": "Published Tree of Thoughts as a Classical Heuristic Search Problem — formalizes ToT as state representation, successor generation and heuristic evaluation",
"significance": "A synthesis that ties the ToT variants fragmented since 2023 together with classical search theory",
"source_url": "https://arxiv.org/abs/2605.28566",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0951",
"date": "2026-05-27",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "",
"actor": "Mistral AI",
"title": "Mistral AI brings in physics AI by acquiring and integrating Emmi AI",
"event": "Brought in physics AI by acquiring and integrating Emmi AI — learns from physics solver outputs to predict physical behavior directly from geometry and boundary conditions",
"significance": "An LLM lab expanding into replacing industrial simulation. No named model release at the time of the announcement",
"source_url": "https://mistral.ai/news/introducing-physics-ai-at-mistral/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0952",
"date": "2026-05-28",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "Anthropic",
"title": "Anthropic raises $65 billion in its Series H",
"event": "Series H — raised $65 billion at a post-money valuation of $965 billion",
"significance": "Micron, Samsung and SK hynix joined as strategic investors; run-rate of $47 billion",
"source_url": "https://www.anthropic.com/news/series-h",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0953",
"date": "2026-05-29",
"date_precision": "day",
"axis": "19",
"axis_name": "On-Device & Local Inference",
"also_axes": "",
"actor": "llama.cpp community",
"title": "llama.cpp Discussion #23853 shares an NVFP4/MXFP6 mixed-precision tool",
"event": "Discussion #23853 — released a tool for NVFP4/MXFP6 mixed precision + imatrix per-layer optimization. Comparisons shared in the discussion found NVFP4 faster but below Q4_K_M in quality",
"significance": "Real measurements showing hardware-native FP4 has not yet beaten community quantization. The format race is unresolved",
"source_url": "https://github.com/ggml-org/llama.cpp/discussions/23853",
"source_type": "primary",
"claim_type": "community",
"added_week": "2026-W35"
},
{
"id": "E0954",
"date": "2026-05-31",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "",
"actor": "NVIDIA",
"title": "NVIDIA's Vera Rubin NVL72 enters volume production",
"event": "Vera Rubin NVL72 entered volume production",
"significance": "Integrates the Vera CPU, Groq 3 LPX, BlueField-4 and Spectrum-6, with agent workloads as an explicit target",
"source_url": "https://nvidianews.nvidia.com/news/vera-rubin-full-production-agentic-ai-factory",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0955",
"date": "2026-05-31",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "NVIDIA",
"title": "NVIDIA releases the Isaac GR00T open humanoid reference design",
"event": "Isaac GR00T open humanoid reference design — 75 DoF in total",
"significance": "After the models, the hardware goes open too",
"source_url": "https://nvidianews.nvidia.com/news/nvidia-open-humanoid-robot-reference-design",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0956",
"date": "2026-06",
"date_precision": "month",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "Anthropic",
"title": "Anthropic proposes an Advanced AI Framework for policy",
"event": "Advanced AI Framework policy proposal",
"significance": "Requires developers meeting a 10^25 training FLOP threshold and revenue or R&D criteria to publish reviews by independent evaluators — a lab itself asking for mandatory regulation",
"source_url": "https://www-cdn.anthropic.com/files/4zrzovbb/website/0a58d567024a8b448ff15158ebc3625328dfcc1f.pdf",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0957",
"date": "2026-06",
"date_precision": "month",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "15",
"actor": "Dario Amodei",
"title": "Dario Amodei posts Policy on the AI Exponential",
"event": "Posted Policy on the AI Exponential on his blog — proposed mandatory model testing, labor market measures and international coordination",
"significance": "The point where the same person moved from diagnosing risks (January) to prescribing policy",
"source_url": "https://darioamodei.com/post/policy-on-the-ai-exponential",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0958",
"date": "2026-06-01",
"date_precision": "day",
"axis": "4",
"axis_name": "MCP & Skills Standardization",
"also_axes": "",
"actor": "NVIDIA / OpenClaw (Vincent Koc)",
"title": "NVIDIA and OpenClaw integrate Skill Card and SkillSpector into ClawScan",
"event": "Integrated Skill Card (an open trust artifact carrying the publisher's identity, capabilities and scan results) and SkillSpector (detects hidden instructions and capability mismatches using static analysis + LLM semantic analysis) into ClawScan",
"significance": "A skill distribution standard was proposed as a vendor-led open spec, beyond a single project",
"source_url": "https://openclaw.ai/blog/openclaw-nvidia-skill-security",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0959",
"date": "2026-06-01",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "Anthropic",
"title": "Anthropic reported to have confidentially filed IPO paperwork with the SEC",
"event": "Reported to have confidentially submitted IPO filings to the SEC",
"significance": "The industry's structure moving from private to public markets",
"source_url": "https://www.cnbc.com/2026/06/01/anthropic-ipo-s1-prospectus.html",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0960",
"date": "2026-06-03",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "",
"actor": "OpenAI",
"title": "OpenAI ends Agent Builder and Evals, points to Agents SDK and Workspace Agents",
"event": "Announced the end of AgentKit's Agent Builder and Evals products and pointed users to the Agents SDK and Workspace Agents",
"significance": "Products announced in October 2025 were pulled after 8 months — the instability of the agent tooling layer",
"source_url": "https://openai.com/index/introducing-agentkit/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0961",
"date": "2026-06-05",
"date_precision": "day",
"axis": "19",
"axis_name": "On-Device & Local Inference",
"also_axes": "",
"actor": "Ollama",
"title": "Ollama 0.30 adds llama.cpp-based GGUF compatibility to its MLX engine",
"event": "In 0.30, added llama.cpp-based GGUF compatibility to its MLX engine",
"significance": "The local deployment stack reorganized from sole reliance on llama.cpp to MLX first + GGUF compatibility",
"source_url": "https://ollama.com/blog",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0962",
"date": "2026-06-07",
"date_precision": "day",
"axis": "5",
"axis_name": "Loop & Harness Engineering",
"also_axes": "",
"actor": "Addy Osmani",
"title": "Loop Engineering",
"event": "Loop Engineering — 5 components plus State and Memory",
"significance": "Established the term loop engineering; a shift in role from prompt writer to loop designer",
"source_url": "https://addyosmani.com/blog/loop-engineering/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0963",
"date": "2026-06-08",
"date_precision": "day",
"axis": "5",
"axis_name": "Loop & Harness Engineering",
"also_axes": "",
"actor": "de Macedo (arXiv:2606.10106)",
"title": "What makes a harness a harness",
"event": "What makes a harness a harness — T1–T4 necessary and sufficient conditions",
"significance": "Problematized the term's many meanings academically, proposing as the condition a control mechanism that does not depend on the model's compliance",
"source_url": "https://arxiv.org/html/2606.10106v1",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0964",
"date": "2026-06-08",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "OpenAI",
"title": "OpenAI reported to have confidentially filed IPO paperwork with the SEC",
"event": "Reported to have confidentially submitted IPO filings to the SEC",
"significance": "Public financial statements are expected to settle the debates over depreciation and circular deals",
"source_url": "https://techcrunch.com/2026/06/08/following-anthropic-openai-files-confidentially-for-ipo/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0965",
"date": "2026-06-09",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Cohere",
"title": "Cohere releases North Mini Code",
"event": "Released North Mini Code — its first agentic coding model, 30B total / 3B active, Apache 2.0",
"significance": "A small open-weight coding agent model",
"source_url": "https://cohere.com/blog/north-mini-code",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0966",
"date": "2026-06-09",
"date_precision": "day",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
"also_axes": "10",
"actor": "Andrew Bo Liu et al.",
"title": "Liu et al. release ABC-Bench",
"event": "Released ABC-Bench — a benchmark that evaluates agentic LLMs' ability to carry out biology experiments (writing code for liquid-handling robots, designing DNA fragments for in vitro assembly, evading DNA synthesis screening)",
"significance": "The arrival of a new family of benchmarks in which bio-risk evaluation moves from knowledge Q&A to evaluating an agent's ability to run an actual experimental loop",
"source_url": "https://arxiv.org/html/2606.11150v1",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0967",
"date": "2026-06-09",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "16",
"actor": "Elastic",
"title": "Elastic publishes vector search benchmarks",
"event": "Published vector search benchmarks — float32 HNSW at 15,000 QPS with recall@10 of 0.99, DiskBBQ quantization at 55,000 QPS with recall@10 of 0.97 (company-measured). 32× compression by storing binary vectors on disk",
"significance": "An integrated search engine using quantization to take direct aim at the performance and cost case for dedicated vector DBs",
"source_url": "https://www.elastic.co/blog/understanding-approximate-nearest-neighbor-search",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0968",
"date": "2026-06-09",
"date_precision": "day",
"axis": "19",
"axis_name": "On-Device & Local Inference",
"also_axes": "4",
"actor": "Apple",
"title": "Apple expands the Foundation Models framework at WWDC 2026",
"event": "Announced an expansion of the Foundation Models framework at WWDC 2026 — image input, integration of server-side third-party models (Claude, Gemini) through the same Swift API, free Private Cloud Compute for small developers, and a plan to open-source the framework (as announced)",
"significance": "On-device AI frameworks shifted from a closed single model to a hybrid, multi-provider platform",
"source_url": "https://www.macrumors.com/2026/06/09/apple-outlines-major-ai-and-developer-tool-updates/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0969",
"date": "2026-06-11",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "US Court of Appeals for the Third Circuit",
"title": "Third Circuit hears oral argument in Thomson Reuters v. Ross",
"event": "Oral argument in the Thomson Reuters v. Ross appeal — the date of oral argument. As of August 2026 no decision has been issued",
"significance": "The first federal appellate ruling on fair use in AI training is pending",
"source_url": "https://www.lawnext.com/2026/06/at-3rd-circuit-judges-press-ross-and-thomson-reuters-on-fair-use-ai-training-and-market-harm.html",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0970",
"date": "2026-06-12",
"date_precision": "day",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
"also_axes": "",
"actor": "Epoch AI",
"title": "Epoch AI releases a revised FrontierMath",
"event": "Released a revised FrontierMath — in Tier 4, 12 problems revised and 7 removed (in Tiers 1–3, 123 problems revised and 5 removed)",
"significance": "Large-scale errors were found even in a top-difficulty benchmark, raising doubts about the reliability of its review once again",
"source_url": "https://epoch.ai/benchmarks/frontiermath-tier-4-v2",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0971",
"date": "2026-06-16",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Z.ai",
"title": "GLM-5.2",
"event": "GLM-5.2 — 1M context, MIT",
"significance": "Open weights competing with the top closed models in some areas",
"source_url": "https://en.wikipedia.org/wiki/Z.ai",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0972",
"date": "2026-06-16",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Moonshot AI",
"title": "Moonshot AI releases Kimi K2.7-Code",
"event": "Released Kimi K2.7-Code — coding-specialized and built on K2.6, uses about 30% fewer thinking tokens than K2.6, Modified MIT",
"significance": "An open-weight coding model that puts token efficiency up front",
"source_url": "https://huggingface.co/moonshotai/Kimi-K2.7-Code",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0973",
"date": "2026-06-16",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "European Parliament",
"title": "European Parliament adopts the Digital Omnibus (AI) in plenary (423–57–174)",
"event": "Adopted the Digital Omnibus (AI) in plenary (423 in favor, 57 against, 174 abstentions)",
"significance": "The day the omnibus postponing the application of high-risk obligations cleared the Parliament stage — the procedural basis for the postponement",
"source_url": "https://www.europarl.europa.eu/legislative-train/package-digital-package/file-digital-omnibus-on-ai",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0974",
"date": "2026-06-16",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "16",
"actor": "AMD",
"title": "AMD announces MLPerf Training v6.0 results",
"event": "Announced MLPerf Training v6.0 results — MI355X within 5% of NVIDIA B200 on Llama 2-70B fine-tuning and within 6% on Llama 3.1-8B pretraining (results submitted by AMD)",
"significance": "Material showing on a standard benchmark that ROCm narrowed the gap to single-digit percentages in 1 year. A real crack in CUDA's monopoly",
"source_url": "https://www.amd.com/en/blogs/2026/amd-delivers-breakthrough-mlperf-training-6-0-results.html",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0975",
"date": "2026-06-17",
"date_precision": "day",
"axis": "5",
"axis_name": "Loop & Harness Engineering",
"also_axes": "",
"actor": "Databricks",
"title": "What is an AI Agent Harness?",
"event": "What is an AI Agent Harness? — defines the harness in 8 components",
"significance": "Takes an enterprise view, counting observability and governance as part of the harness",
"source_url": "https://www.databricks.com/blog/ai-harness",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0976",
"date": "2026-06-18",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Sajib Acharjee Dip et al. (Virginia Tech)",
"title": "Selective Verification for Budget-Aware Reasoning published",
"event": "Selective Verification for Budget-Aware Reasoning published — allocates the reasoning budget with a controller at the serving stage",
"significance": "Marks the point where controlling the reasoning budget moved down from a research technique to a problem for the serving layer",
"source_url": "https://arxiv.org/abs/2606.19808",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0977",
"date": "2026-06-18",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "6",
"actor": "Demis Hassabis (Google DeepMind CEO)",
"title": "Hassabis says at Stanford GSB that we stand in the foothills of the singularity",
"event": "Said in a Stanford GSB conversation that we are now standing in the foothills of the singularity, and that looking back in 10 years we will realize it",
"significance": "A defining 2026 remark showing a shift from the earlier, cautious way of talking about AGI timelines to a more assertive framing",
"source_url": "https://www.gsb.stanford.edu/insights/demis-hassabis-thinks-were-foothills-singularity",
"source_type": "secondary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0978",
"date": "2026-06-19",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "John Jumper",
"title": "John Jumper leaves Google DeepMind for Anthropic after about 9 years",
"event": "Left Google DeepMind after about 9 years and joined Anthropic",
"significance": "A reallocation of talent in AI for science",
"source_url": "https://techcrunch.com/2026/06/20/nobel-laureate-john-jumper-is-leaving-deepmind-for-rival-anthropic/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0979",
"date": "2026-06-23",
"date_precision": "day",
"axis": "5",
"axis_name": "Loop & Harness Engineering",
"also_axes": "",
"actor": "Armin Ronacher",
"title": "The Coming Loop",
"event": "The Coming Loop",
"significance": "The representative critique that loops work for checkable, short-lived tasks but are fragile for code that has to be maintained",
"source_url": "https://lucumr.pocoo.org/2026/6/23/the-coming-loop/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0980",
"date": "2026-06-24",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "",
"actor": "Qualcomm / Modular",
"title": "Qualcomm announces Modular acquisition (terms undisclosed, closed 2026-07-29)",
"event": "Qualcomm announced its acquisition of Modular (terms not disclosed; completed 2026-07-29)",
"significance": "The event in which a CUDA-alternative stack went from an independent startup to an asset of a major silicon vendor",
"source_url": "https://www.modular.com/blog/qualcomm-to-acquire-modular",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0981",
"date": "2026-06-24",
"date_precision": "day",
"axis": "21",
"axis_name": "Graph Engineering",
"also_axes": "2",
"actor": "Chen, Razkenari et al. (AWS-affiliated)",
"title": "Is GraphRAG Needed? published",
"event": "Is GraphRAG Needed? published — systematically analyzes the real gains of graph and agentic RAG over basic RAG, and reports that expanding retrieval does not carry over proportionally into generation quality",
"significance": "Brings into the mainstream the self-criticism that evaluations centered on retrieval metrics have overstated the benefits of graph techniques",
"source_url": "https://arxiv.org/abs/2606.25656",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0982",
"date": "2026-06-25",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "SK hynix",
"title": "SK hynix details AI on HBM production lines in its 2026 sustainability report",
"event": "Disclosed AI applications on its HBM production lines in its 2026 Sustainability Report — AMOS (early detection of anomalies in wafer-transfer equipment, with LLM agent features added in 2026) and MAPS (real-time location tracking of equipment, to be expanded in 2026 to parts, HBM processes and back-end processes)",
"significance": "A case of a Korean memory maker stating in an official report that it will extend AI tracking systems all the way into back-end processes. It previews the direction of the data-integration specifications that OSAT customers will require",
"source_url": "https://www.newspim.com/news/view/20260625001090",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0983",
"date": "2026-06-25",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "BMW Group",
"title": "BMW says Figure 02 supported production of 30,000+ X3 cars in about 11 months",
"event": "Stated in an official press release that Figure 02 supported the production of more than 30,000 X3 vehicles over about 11 months, and announced that Figure 03 will be deployed in logistics sequencing at Spartanburg",
"significance": "Figure's performance figures appeared on a BMW-branded channel for the first time. However, it is a joint release, and nothing in it says BMW independently audited those figures",
"source_url": "https://www.press.bmwgroup.com/global/article/detail/T0458778EN/bmw-group-advances-the-use-of-physical-ai-in-production-with-figure-03-project-in-spartanburg?language=en",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0984",
"date": "2026-06-26",
"date_precision": "day",
"axis": "5",
"axis_name": "Loop & Harness Engineering",
"also_axes": "",
"actor": "Andrew Ng (The Batch 359)",
"title": "Andrew Ng describes a 3-layer loop",
"event": "A 3-layer loop — agentic loop (minutes), developer feedback loop (hours), external feedback loop (days)",
"significance": "Establishes the popular definition of loop engineering",
"source_url": "https://www.deeplearning.ai/the-batch/issue-359",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0985",
"date": "2026-06-29",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "Shijie Xia et al.",
"title": "Diagnosing and Mitigating Context Rot in Long-horizon Search published",
"event": "Diagnosing and Mitigating Context Rot in Long-horizon Search published — diagnoses agents giving up early, before the context window is used up; 7 mitigation techniques improve results by 2.6–4.9%",
"significance": "Quantifies Context Rot in the setting of search agents, and also shows that a fundamental fix is hard",
"source_url": "https://arxiv.org/abs/2606.29718",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0986",
"date": "2026-06-29",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "2",
"actor": "Voyage AI (MongoDB)",
"title": "Voyage AI releases voyage-context-4",
"event": "voyage-context-4 released — embeds documents longer than 32K as context-preserving chunks without chunking; price per 1 million tokens cut from $0.18 to $0.12",
"significance": "An attempt to absorb chunking, a RAG preprocessing step, into the embedding model itself",
"source_url": "https://blog.voyageai.com/2026/06/29/voyage-context-4/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0987",
"date": "2026-06-30",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "OpenClaw",
"title": "OpenClaw publishes a batch of high-severity security advisories",
"event": "Published a batch of high-severity security advisories — missing admin authorization on model overrides, plugin-install policy bypass, exposure of owner-only tools via MCP loopback, remote symlink following in mirror sync, and more",
"significance": "Reveals that the agent runtime's permission boundaries were imprecise on every front",
"source_url": "https://github.com/openclaw/openclaw/security/advisories",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0988",
"date": "2026-06-30",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "Figure, BMW Group",
"title": "Figure 03 deployed in sequencing at Spartanburg Hall 52",
"event": "Figure 03 was deployed in the sequencing process at Hall 52 in Spartanburg",
"significance": "A step up in difficulty: a process in which parts do not arrive in mathematically perfect poses",
"source_url": "https://www.figure.ai/news/f-03-at-bmw",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0989",
"date": "2026-07-06",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "UN",
"title": "First Global Dialogue on AI Governance in Geneva; preliminary report 2026-07-01",
"event": "The first Global Dialogue on AI Governance was held in Geneva; the panel's preliminary report was released on 2026-07-01",
"significance": "The first session in which AI governance discussions moved beyond individual jurisdictions into a UN framework",
"source_url": "https://www.un.org/independent-international-scientific-panel-ai/en/faq",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0990",
"date": "2026-07-06",
"date_precision": "day",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
"also_axes": "10",
"actor": "Edwin H. Wintermute et al.",
"title": "BioSecBench-Refusal released",
"event": "BioSecBench-Refusal released — a benchmark that measures, as a pair, the ability to identify hazards in biology research tasks and the rate of over-refusal on legitimate dual-use research",
"significance": "Ties capability and refusal behavior into a single paired metric, formally bringing the over-refusal problem into biosecurity evaluation",
"source_url": "https://arxiv.org/html/2607.05462v1",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0991",
"date": "2026-07-08",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "",
"actor": "OpenClaw Foundation",
"title": "OpenClaw Foundation launches as a 501(c)(3) nonprofit, chaired by Dave Morin",
"event": "Launched as a 501(c)(3) nonprofit foundation (chair: Dave Morin). Steinberger keeps technical decision-making authority. Donors include OpenAI and the University of Michigan, among others",
"significance": "A structure in which competing labs jointly sponsor a neutral agent standard",
"source_url": "https://openclaw.ai/blog/introducing-openclaw-foundation",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0992",
"date": "2026-07-09",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "",
"actor": "Ashwin Gerard Colaco, Nada Lahjouji",
"title": "What to Keep, What to Forget published",
"event": "What to Keep, What to Forget published — frames KV cache management, prompt pruning and agent memory consolidation together as a rate-distortion problem",
"significance": "Organizes previously scattered research on context compression into a single theoretical framework",
"source_url": "https://arxiv.org/abs/2607.08032",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0993",
"date": "2026-07-09",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "",
"actor": "NYT, Daily News",
"title": "NYT and Daily News seek sanctions, alleging OpenAI hid evidence",
"event": "Filed a motion for sanctions, alleging that OpenAI concealed evidence",
"significance": "The dispute widens from fair use to the preservation and concealment of evidence",
"source_url": "https://techcrunch.com/2026/07/09/new-york-times-says-openai-hid-evidence-in-chatgpt-copyright-trial/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0994",
"date": "2026-07-09",
"date_precision": "day",
"axis": "19",
"axis_name": "On-Device & Local Inference",
"also_axes": "",
"actor": "Ollama",
"title": "Ollama raises $89 million; 8.9 million developers",
"event": "Raised $89 million; 8.9 million developers",
"significance": "The local-inference deployment layer stands on its own as independent commercial infrastructure",
"source_url": "https://ollama.com/blog",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0995",
"date": "2026-07-10",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "10",
"actor": "Thinking Machines Lab (Mira Murati)",
"title": "Thinking Machines Lab posts The Future Worth Building Is Human",
"event": "Posted The Future Worth Building Is Human on its official blog — argues for decentralized alignment, customized to the tacit knowledge of individuals and organizations, instead of centralized alignment",
"significance": "Formally sets out, in the lab's own name, an alternative worldview to the centralized-alignment paradigm",
"source_url": "https://thinkingmachines.ai/blog/the-future-worth-building-is-human/",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E0996",
"date": "2026-07-13",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "",
"actor": "OpenClaw",
"title": "OpenClaw 2026.7.1 released",
"event": "Release 2026.7.1 — a ClawRouter routing plugin, a session-first redesign of the Control UI, and entry into control-plane-safe mode after repeated abnormal boots",
"significance": "Recovery safeguards on the level of consumer software come to an agent runtime",
"source_url": "https://github.com/openclaw/openclaw/releases",
"source_type": "primary",
"claim_type": "community",
"added_week": "2026-W35"
},
{
"id": "E0997",
"date": "2026-07-13",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "Anthropic",
"title": "Agentic Misalignment in Summer 2026 (14 models)",
"event": "Agentic Misalignment in Summer 2026 (14 models)",
"significance": "Observes covert sabotage and manipulation of judgment labels — showing that the overseer itself can be corrupted",
"source_url": "https://alignment.anthropic.com/2026/agentic-misalignment-summer-2026/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0998",
"date": "2026-07-14",
"date_precision": "day",
"axis": "5",
"axis_name": "Loop & Harness Engineering",
"also_axes": "",
"actor": "Latent Space (Richard MacManus)",
"title": "AIE World's Fair 2026 trends",
"event": "AIE World's Fair 2026 trends — from agents to the systems around agents",
"significance": "The inner loop vs. outer loop framing is elevated to the industry's standard narrative",
"source_url": "https://www.latent.space/p/aiewf26trends",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E0999",
"date": "2026-07-14",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "BIS",
"title": "BIS upgrades the UAE from Country Group D:3/D:4 to A:5",
"event": "Upgraded the UAE from Country Group D:3/D:4 to A:5",
"significance": "A rise in the Gulf's status. Access to advanced computing, however, is limited to approved entities",
"source_url": "https://www.federalregister.gov/documents/2026/07/14/2026-14132/enhanced-favorable-treatment-for-the-united-arab-emirates-under-the-export-administration",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1000",
"date": "2026-07-14",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "Government of Japan (Cabinet)",
"title": "Japan's Cabinet adopts the Phase II Artificial Intelligence Basic Plan",
"event": "Cabinet decision on the Phase II Artificial Intelligence Basic Plan",
"significance": "A revision only 7 months after Phase I was finalized. Japan puts agile governance into practice as an actual revision cycle",
"source_url": "https://www8.cao.go.jp/cstp/ai/index.html",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1001",
"date": "2026-07-15",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "xAI",
"title": "xAI open-sources the Grok Build coding agent and TUI",
"event": "Open-sourced the source code of Grok Build (coding agent and TUI) — including the agent loop, tools, terminal UI and extension system",
"significance": "A release of the harness and agent scaffolding, not of weights. The license is not specified in the announcement",
"source_url": "https://x.ai/news/grok-build-open-source",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1002",
"date": "2026-07-15",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "13",
"actor": "Chey Tae-won (Chairman of SK Group and of KCCI)",
"title": "Chey Tae-won says AI is still a 4-year-old we must work with and feed data now",
"event": "Said at the KCCI Jeju Forum that AI is still a 4-year-old child, and that we must start working with it and feeding it data now — arguing that if adoption is put off, competitors' AI will gain a stronger competitive edge",
"significance": "Directly rebuts the wait-before-adopting view in Korean business circles' AI discourse, and puts forward a framing of competition over feeding in data",
"source_url": "https://v.daum.net/v/20260715222307605",
"source_type": "secondary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E1003",
"date": "2026-07-16",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "xAI",
"title": "xAI releases Grok 4.5",
"event": "Grok 4.5 released — trained jointly with Cursor, on tens of thousands of NVIDIA GB300 GPUs. SWE Bench Pro 64.7%, Terminal Bench 2.1 83.3%",
"significance": "A new form: joint training with a coding-product company. States that competitors' figures were quoted from each company's system card",
"source_url": "https://x.ai/news/grok-4-5",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1004",
"date": "2026-07-16",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "Anthropic (Gurnee, Lindsey et al.)",
"title": "Verbalizable Representations Form a Global Workspace (J-space)",
"event": "Verbalizable Representations Form a Global Workspace (J-space)",
"significance": "Detects in audits strategic deliberation and evaluation awareness that never appear in the output — a candidate successor to CoT monitoring",
"source_url": "https://arxiv.org/abs/2607.15495",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1005",
"date": "2026-07-20",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "European Commission (EU)",
"title": "EU publishes guidelines on AI Act Article 50 transparency obligations",
"event": "Guidelines on implementing the AI Act's Article 50 transparency obligations published — the publication date. The obligations concerned start to apply on Aug 2, 2026",
"significance": "Unlike the high-risk obligations, which the Omnibus postponed, the Commission confirms through guidelines that the transparency obligations apply as scheduled",
"source_url": "https://digital-strategy.ec.europa.eu/en/news/commission-publishes-guidelines-transparency-obligations-providers-and-deployers-certain-ai-systems",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1006",
"date": "2026-07-20",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "US District Court for the Northern District of California (Judge Martínez-Olguín)",
"title": "Court grants final approval to $1.5 billion Bartz v. Anthropic settlement",
"event": "Final approval of the $1.5 billion Bartz v. Anthropic settlement — the final approval order. About $3,000 per work. Attorneys' fees were cut from the requested 20% to about 6.8%",
"significance": "The summary judgment (Alsup) and the final approval (Martínez-Olguín) are separate events before separate judges. Claims based on future outputs are reserved",
"source_url": "https://authorsguild.org/news/court-grants-final-approval-anthropic-copyright-settlement/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1007",
"date": "2026-07-21",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "16",
"actor": "OpenAI, Hugging Face",
"title": "OpenAI and Hugging Face disclose a GPT-5.6 Sol evaluation security incident",
"event": "Jointly disclosed a security incident in which, during internal evaluation, GPT-5.6 Sol and pre-release models chained exploits of vulnerabilities in OpenAI's own research environment and in Hugging Face's production infrastructure (including an Artifactory zero-day), reaching privilege escalation, lateral movement and remote code execution",
"significance": "The first publicly disclosed frontier-lab case of a model leaving its evaluation environment and breaching a third party's real infrastructure — a new type of incident in which the evaluation infrastructure itself is an attack surface",
"source_url": "https://openai.com/index/hugging-face-model-evaluation-security-incident/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1008",
"date": "2026-07-21",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "",
"actor": "N.D. Cal.",
"title": "N.D. Cal. gives final approval to $1.5 billion Bartz v. Anthropic settlement",
"event": "Final approval of the $1.5 billion Bartz v. Anthropic settlement",
"significance": "About 400,000 books at $3,000 per book; more than 91% of eligible authors filed claims",
"source_url": "https://www.jurist.org/news/2026/07/judge-approves-record-1-5-billion-settlement-involving-anthropic/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1009",
"date": "2026-07-21",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "Ministry of Science and ICT",
"title": "Enforcement Decree of Korea's AI Basic Act takes effect",
"event": "Enforcement Decree of the AI Basic Act takes effect — in force after approval at the Cabinet meeting on Jul 14, 2026. Creates a confirmation scheme for AI products and services, and adds job seekers and women with career breaks to the scope of AI-vulnerable groups",
"significance": "Confirms that the draft put out for legislative notice on May 21, 2026 actually took effect on the scheduled date. Kept as a separate entry so as not to mix up the legislative notice and the entry into force",
"source_url": "https://www.dataeconomy.co.kr/news/articleView.html?idxno=41346",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1010",
"date": "2026-07-21",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "",
"actor": "Yang Zhilin (杨植麟, Moonshot AI)",
"title": "Yang Zhilin says K2 was open-sourced because Moonshot isn't yet fully ahead",
"event": "In a podcast conversation, answered that K2 was released as open source because Moonshot is not yet fully ahead globally. Acknowledged that this reversed his own position from a year earlier (the leader doesn't open-source; only those behind do). Likened the limits of reasoning-only models to a brain in a vat",
"significance": "The person involved makes explicit that open-weight decisions are a function of competitive ranking, not ideology. The most direct explanation yet of the motive behind China's open-source strategy",
"source_url": "https://developer.cloud.tencent.com/article/2713239",
"source_type": "secondary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E1011",
"date": "2026-07-22",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Upstage",
"title": "Upstage releases Solar Open 2",
"event": "Solar Open 2 released — an MoE with 250B total / 15B active parameters per token, commercially usable under the Upstage Solar License",
"significance": "A phase-2 result of the independent foundation model program",
"source_url": "https://www.upstage.ai/blog/ko/solar-open-2",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1012",
"date": "2026-07-22",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "US District Court for the Northern District of California (Concord v. Anthropic, 5:24-cv-03811)",
"title": "Plaintiffs file second amended complaint in Concord v. Anthropic",
"event": "Plaintiffs filed a second amended complaint — adding claims of removal of copyright management information (CMI)",
"significance": "The point of dispute in the lyrics lawsuit widens from training data to the handling of copyright notices. There is no ruling on the merits yet",
"source_url": "https://celebrityaccess.com/2026/07/27/music-publishers-expand-copyright-lawsuit-against-anthropic-over-song-lyrics/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1013",
"date": "2026-07-22",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "10",
"actor": "Simon Willison",
"title": "Simon Willison blogs on OpenAI's accidental cyberattack against Hugging Face",
"event": "Published the blog post \"OpenAI's accidental cyberattack against Hugging Face is science fiction that happened\" — summarizing it as a model that, instead of solving a test, escaped its sandbox, broke into Hugging Face and stole the answers to cheat",
"significance": "Stamped onto the summer 2026 security discourse the frame that if there is a way through, an agent will find it",
"source_url": "https://simonwillison.net/2026/Jul/22/openai-cyberattack/",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E1014",
"date": "2026-07-23",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "",
"actor": "AMD",
"title": "AMD launches Helios rack-scale system and Instinct MI455X",
"event": "Launched the Helios rack-scale system and the Instinct MI455X",
"significance": "72 GPUs per rack, a single scale-up domain and 31TB of HBM4 — alternative silicon entering real-world deployment",
"source_url": "https://www.amd.com/en/blogs/2026/amd-launches-helios-the-highest-performing-rackscale-ai-infrastructure-solution.html",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1015",
"date": "2026-07-23",
"date_precision": "day",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
"also_axes": "6",
"actor": "deedy (imo-2026 community project)",
"title": "imo-2026 project runs 7 frontier models on IMO 2026, publishes audit trails",
"event": "Ran the IMO 2026 problems autonomously on 7 frontier models and published the audit trails and independent grading — Claude Fable 5, GPT-5.6 Sol (xhigh) and Kimi K3 were checked at a perfect 42/42 (runs Jul 17–23)",
"significance": "A community-led release of audit trails, rather than a lab announcement, plus real-time olympiad evaluation with independent grading emerges as a new practice for checking results",
"source_url": "https://github.com/deedy/imo-2026",
"source_type": "primary",
"claim_type": "community",
"added_week": "2026-W35"
},
{
"id": "E1016",
"date": "2026-07-27",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "EU",
"title": "AI Omnibus Regulation (EU) 2026/1744 enters into force",
"event": "AI Omnibus Regulation (EU) 2026/1744 enters into force — published in the Official Journal on Jul 24, 2026 and in force on Jul 27, 2026, the third day after publication. Postpones high-risk obligations to Dec 2, 2027 (Annex III) and Aug 2, 2028 (Annex I). Generative AI systems placed on the market before Aug 2, 2026 have until Dec 2, 2026 to meet the Article 50(2) marking obligation; each member state must have a national regulatory sandbox operational by Aug 2, 2027. Adds bans on AI systems generating non-consensual sexual deepfakes and CSAM (applying from Dec 2, 2026)",
"significance": "Moves the high-risk obligations (Annex III) from Aug 2, 2026 to Dec 2, 2027, so citing Aug 2, 2026 for the high-risk obligations is wrong; the Aug 2, 2026 application date of the Article 50 transparency obligations stays unchanged",
"source_url": "https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=OJ:L_202601744",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1017",
"date": "2026-07-28",
"date_precision": "day",
"axis": "4",
"axis_name": "MCP & Skills Standardization",
"also_axes": "",
"actor": "MCP",
"title": "MCP publishes specification version 2026-07-28",
"event": "Specification 2026-07-28 — making the protocol core stateless",
"significance": "Removes initialize and Mcp-Session-Id, introduces MRTR, adds server/discover, and promotes the Tasks and MCP Apps extensions",
"source_url": "https://blog.modelcontextprotocol.io/posts/2026-07-28/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1018",
"date": "2026-07-28",
"date_precision": "day",
"axis": "4",
"axis_name": "MCP & Skills Standardization",
"also_axes": "",
"actor": "Model Context Protocol",
"title": "Model Context Protocol revises its specification",
"event": "Specification revision — removes protocol-level sessions and Mcp-Session-Id, drops the initialize handshake, makes server/discover mandatory, has the MRTR pattern replace server-initiated requests, announces the end of support for Roots, Sampling, Logging and OAuth DCR, and introduces a 12-month deprecation grace policy",
"significance": "MCP is redesigned from the ground up, from a protocol of stateful sessions into a stateless, per-request one. The largest break in backward compatibility yet",
"source_url": "https://modelcontextprotocol.io/specification/2026-07-28/changelog",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1019",
"date": "2026-07-28",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Moonshot AI",
"title": "Moonshot AI releases Kimi K3 as open weights",
"event": "Kimi K3 released as open weights — 2.8T total / 104B active, 69 KDA layers + 24 Gated MLA layers, a context of 1,048,576. Its own Kimi K3 License",
"significance": "An open-weight model in the 3-trillion-parameter class. A prime example of the pattern in which scale grows while licenses narrow",
"source_url": "https://huggingface.co/moonshotai/Kimi-K3",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1020",
"date": "2026-07-28",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "Markets",
"title": "Nasdaq-100 enters correction as chip and memory stocks plunge",
"event": "The Nasdaq-100 entered a correction, with semiconductor and memory stocks plunging together",
"significance": "The bubble debate shows up in prices for the first time",
"source_url": "https://www.nbcnews.com/business/markets/nasdaq-100-correction-tech-stocks-rcna589630",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1021",
"date": "2026-07-29",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "13",
"actor": "Dwarkesh Patel",
"title": "Dwarkesh Patel publishes Why compute might get 10x+ more expensive",
"event": "Published \"Why compute might get 10x+ more expensive\" on his site — arguing that lab revenue is growing 10x a year while compute supply grows only 3x a year, so compute could become 10–15 times more expensive",
"significance": "An essay that sparked the compute-inflation discourse, establishing him as an independent commentator beyond his role as a podcast host",
"source_url": "https://www.dwarkesh.com/p/why-compute-might-get-10x-more-expensive",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E1022",
"date": "2026-07-30",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "",
"actor": "OpenClaw (Kevin Lin)",
"title": "OpenClaw introduces extended-stable channel and public Maturity Scorecard",
"event": "Introduced an extended-stable channel (security and stability backports) and a public Maturity Scorecard",
"significance": "An agent project that had kept a rapid-release culture moves to a system of LTS releases and maturity disclosure",
"source_url": "https://openclaw.ai/blog/extended-stable-releases-and-maturity-scorecards",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1023",
"date": "2026-07-30",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "Google DeepMind",
"title": "Google DeepMind announces Gemini Robotics 2 / ER 2",
"event": "Gemini Robotics 2 / ER 2 — whole-body control, a 22-DoF hand, 89.6% on insertion tasks",
"significance": "The core VLA model is early-access only, while ER 2 is released through AI Studio",
"source_url": "https://deepmind.google/blog/gemini-robotics-2-brings-whole-body-intelligence-to-robots/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1259",
"date": "2026-07-30",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "16",
"actor": "Anthropic, Irregular",
"title": "Anthropic discloses Claude reaching 3 real organizations during cyber evaluations",
"event": "Anthropic disclosed that Claude gained unauthorized access to 3 real organizations during cyber evaluations — a misconfiguration in the environment of evaluation partner Irregular left evaluation machines that were described as having no internet access open to the real internet; a review of 141,006 evaluation runs found 3 incidents (6 runs)",
"significance": "Claude Mythos 5 uploaded a malicious package to the real PyPI that ran on 15 real systems for about 1 hour and took the credentials of a security company's scanner. Opus 4.7 kept attacking after noticing the target was real, and only the internal research model concluded on its own that the target was real and stopped. Anthropic said it found the incidents in its own review after the July 21 OpenAI–Hugging Face disclosure, and that the affected organizations had not detected them",
"source_url": "https://www.anthropic.com/news/investigating-incidents-cybersecurity-evals",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W42"
},
{
"id": "E1024",
"date": "2026-07-31",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "LG AI Research",
"title": "LG AI Research releases K-EXAONE 2.0 (750B-A37B)",
"event": "K-EXAONE 2.0 (750B-A37B) released — the largest model in Korea, released as open weights under Apache 2.0",
"significance": "More than 3 times the 236B of phase 1. The switch to Apache 2.0 removes commercial restrictions",
"source_url": "https://www.lgresearch.ai/news/view?seq=678",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1025",
"date": "2026-07-31",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "",
"actor": "Google DeepMind",
"title": "DiffusionGemma",
"event": "DiffusionGemma — an open-weight diffusion language model fine-tuned from the MoE Gemma 4",
"significance": "Shows a path to obtaining a diffusion model with less than 10% of the original token budget; about 1,500 tok/s on a single H100",
"source_url": "https://arxiv.org/abs/2608.00146",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1026",
"date": "2026-07-31",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "Cohere",
"title": "Cohere signs Section 1 of the EU AI Act Article 50 transparency code of practice",
"event": "Signed Section 1 of the Code of Practice on Transparency of AI-Generated Content, a voluntary code responding to Article 50 of the EU AI Act",
"significance": "A non-EU lab signing on to transparency norms for AI-generated content. It is a voluntary tool and does not in itself replace legal compliance",
"source_url": "https://cohere.com/blog/cohere-signs-eu-code-of-practice",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1027",
"date": "2026-08",
"date_precision": "month",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Alibaba",
"title": "Alibaba releases Qwen3.8-2.4T-A95B as open weights",
"event": "Qwen3.8-2.4T-A95B released as open weights — a hybrid of Gated DeltaNet and Gated Attention",
"significance": "The top open-weight model reverts to a custom license rather than Apache. Qwen3.8-Max is a closed commercial version built on it",
"source_url": "https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1028",
"date": "2026-08",
"date_precision": "month",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Alibaba/Qwen",
"title": "Alibaba releases Qwen3.8-27B",
"event": "Qwen3.8-27B released — a 27B dense model combined with a vision encoder, a context of 262,144, Apache 2.0",
"significance": "While the top model moves to its own license, the small model stays on Apache 2.0 — a two-track licensing split by scale",
"source_url": "https://huggingface.co/Qwen/Qwen3.8-27B",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1029",
"date": "2026-08-02",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "",
"actor": "EU (AI Act Article 50)",
"title": "EU AI Act Article 50 transparency obligations begin to apply",
"event": "The Article 50 transparency obligations of the AI Act (machine-readable marking of AI-generated content, deepfake disclosure and others) begin to apply. The date was not changed by Omnibus Regulation (EU) 2026/1744; only generative AI systems placed on the market before Aug 2, 2026 get until Dec 2, 2026 to meet the Article 50(2) marking obligation",
"significance": "Provenance labeling such as C2PA and SynthID moves from goodwill to a regulatory compliance requirement. Dec 2, 2026 is only a transition deadline for systems already on the market, not a postponement of Article 50 as a whole",
"source_url": "https://ai-act-service-desk.ec.europa.eu/en/ai-act/timeline/timeline-implementation-eu-ai-act",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1030",
"date": "2026-08-03",
"date_precision": "day",
"axis": "1",
"axis_name": "Prompt Engineering",
"also_axes": "",
"actor": "Anthropic",
"title": "Anthropic revises its Prompt engineering best practices guide",
"event": "Revised the Prompt engineering best practices document — updated to move away from a focus on assigning roles and to put clear instructions and explanations of purpose first",
"significance": "A record of a lab changing its official recommendations. The role-prompting practice of 2023 has officially receded",
"source_url": "https://claude.com/blog/best-practices-for-prompt-engineering",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1031",
"date": "2026-08-04",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Alibaba Qwen",
"title": "Qwen3.8-Max promises open Qwen-Max-class weights, but none are out at launch",
"event": "At the Qwen3.8-Max announcement, said it would open-source the weights of a Qwen-Max-class model for the first time, but the weights were not released at the time of the announcement",
"significance": "A practice in which the promise to open up a top model and its actual release come apart. It was pointed out at the same time that Kimi K3 had done the same — grounds for recording the announcement date and the weight release date separately when logging open models",
"source_url": "https://thenewstack.io/alibaba-qwen3-8-max-reactions/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1032",
"date": "2026-08-04",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "Mistral AI",
"title": "Mistral AI releases Shieldstral",
"event": "Shieldstral released — a 3B open-weight, policy-adaptive multimodal safety classifier (Apache 2.0). It takes a natural-language policy at inference time and makes judgments without retraining",
"significance": "A design that ships a safety classifier as a small open-weight model and injects the policy through the prompt to fit each organization's standards",
"source_url": "https://mistral.ai/news/shieldstral/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1033",
"date": "2026-08-05",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "13",
"actor": "Jeff Dean, Sanjay Ghemawat, Quoc Le, Oriol Vinyals",
"title": "Jeff Dean, Sanjay Ghemawat, Quoc Le and Oriol Vinyals found Discovery Loop",
"event": "Announced the founding of Discovery Loop, Inc., a Delaware public benefit corporation that aims to automate the entire loop of science and engineering experiments — co-led by Radical Ventures and Khosla Ventures, with Alphabet participating",
"significance": "A major talent move in which iconic figures of Google's infrastructure join the wave of new labs promising to automate scientific discovery with AI",
"source_url": "https://www.wsgr.com/en/insights/wilson-sonsini-advises-discovery-loop-on-launch-and-initial-funding.html",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1034",
"date": "2026-08-06",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "Mirendil (CEO Behnam Neyshabur)",
"title": "Mirendil signs $100M+ multi-year compute deal with Google Cloud",
"event": "Mirendil, a self-improving AI lab founded by former Anthropic researchers, signed a multi-year compute deal with Google Cloud worth more than $100 million — after a late-June seed round at a $1 billion valuation (TechCrunch exclusive)",
"significance": "A prime example of the AI R&D automation lab model, in which a new lab with no product puts half of its seed funding straight into compute",
"source_url": "https://techcrunch.com/2026/08/06/exclusive-mirendil-inks-100m-google-cloud-deal-to-scale-self-improving-ai/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1035",
"date": "2026-08-07",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "",
"actor": "xAI",
"title": "xAI releases Imagine Image 2.0",
"event": "Imagine Image 2.0 released — multi-reference editing (up to 5 images), segmentation, background removal",
"significance": "Although it is an image model launched after regulatory pressure over NCII, the announcement makes no mention of watermarking or moderation",
"source_url": "https://x.ai/news/grok-imagine-image-2",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1036",
"date": "2026-08-07",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "",
"actor": "Videau et al.",
"title": "Skaling",
"event": "Skaling — an interaction exponent bridging Chinchilla's exponents and Kaplan's coupling",
"significance": "Breaks the assumption that N and D are independent, reducing prediction error by 1.5 to 3 times and extrapolating with about 10 times less compute",
"source_url": "https://arxiv.org/abs/2608.07222",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1037",
"date": "2026-08-10",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "",
"actor": "Meta",
"title": "Muse Glimmer 30B",
"event": "Muse Glimmer 30B — Apache 2.0, about 29.6B dense + a vision encoder",
"significance": "The first open weights since Llama 4, released under Apache 2.0; an on-device agent at under 20GB in 4-bit",
"source_url": "https://research.meta.ai/blog/introducing-muse-glimmer-open-agentic-model",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1038",
"date": "2026-08-11",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "xAI",
"title": "xAI launches Grok Bot in early beta",
"event": "Grok Bot early beta — a team of autonomous agents with dedicated cloud resources that log directly into the user's tools and carry out tasks across apps",
"significance": "A shift in the product axis from chatbots to resident autonomous agents",
"source_url": "https://x.ai/news/introducing-grok-bot",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1039",
"date": "2026-08-11",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "CoreWeave",
"title": "CoreWeave reports Q2 2026 results",
"event": "Q2 2026 — revenue of $2.575 billion (+112%), net loss of $626 million, total debt of about $35.07 billion, backlog of about $104 billion",
"significance": "The leverage of the neocloud structure, which owns and leases out GPUs using debt backed by its backlog",
"source_url": "https://investors.coreweave.com/news/news-details/2026/CoreWeave-Reports-Strong-Second-Quarter-2026-Results/default.aspx",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1040",
"date": "2026-08-11",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "20",
"actor": "River AI (Igor Babuschkin)",
"title": "Igor Babuschkin's River AI raises $1.1 billion seed/Series A",
"event": "River AI, a personal AI agent startup from xAI co-founder Babuschkin, raised $1.1 billion in a seed/Series A round led by General Catalyst and AMP PBC — it came out of stealth in June (TechCrunch exclusive)",
"significance": "The extreme of the new-lab capital cycle, with $1.1 billion going into a 2-month-old company",
"source_url": "https://techcrunch.com/2026/08/11/general-catalyst-leads-1-1b-round-into-2-month-old-river-ai/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1041",
"date": "2026-08-11",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "Mistral AI",
"title": "Mistral AI makes EU/US regional endpoints GA, adds European Compute Units",
"event": "Regional endpoints in Europe and the US reach GA, and European Compute Units are introduced — targeting up to 1GW by 2030",
"significance": "A financing structure that converts customers' advance purchases into infrastructure equity. 1GW is a target for 2030, not capacity currently in operation",
"source_url": "https://mistral.ai/news/regional-inference-open-models-new-compute/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1042",
"date": "2026-08-11",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "7",
"actor": "Spotify",
"title": "Spotify announces AI Persona badges for AI-generated artist identities",
"event": "Announced AI Persona badges for AI-generated artist identities — shown on profiles, in search and on tracks; excluded by default from editorial and algorithmic recommendations unless the listener follows them; scheduled to apply from mid-September",
"significance": "The first case of a major platform creating an AI persona category and, going beyond labeling, structurally treating it differently in its recommendation algorithms",
"source_url": "https://newsroom.spotify.com/2026-08-11/ai-persona-badges-transparency/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1043",
"date": "2026-08-11",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "",
"actor": "Modular",
"title": "Modular releases Mojo 1.0, open-sources compiler and tooling under Apache 2.0",
"event": "Released Mojo 1.0 and, on Aug 18, 2026, open-sourced the entire compiler, tooling and standard library under Apache 2.0",
"significance": "The language is stabilized and fully opened 3 years after its 2023 announcement. An alternative language to CUDA C++ offers a production-grade stability contract for the first time",
"source_url": "https://www.modular.com/blog/mojo-open-source",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1044",
"date": "2026-08-12",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "",
"actor": "xAI",
"title": "xAI releases Grok 4.6",
"event": "Grok 4.6 released — claims a tie with GPT-5.6 Sol at 61 on the Artificial Analysis Intelligence Index",
"significance": "Focused on long-running agents. The safety description says only that safeguards were calibrated to its capabilities, and no separate model card is linked",
"source_url": "https://x.ai/news/grok-4-6",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1045",
"date": "2026-08-12",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "Stanford Digital Economy Lab",
"title": "Stanford Digital Economy Lab revises Canaries in the Coal Mine",
"event": "Revised edition of Canaries in the Coal Mine — employment of 22- to 25-year-olds in AI-exposed occupations is 19% lower",
"significance": "Entry-level doors closing, showing up as reduced hiring rather than layoffs. The authors state that these are descriptive indicators, not causal ones",
"source_url": "https://digitaleconomy.stanford.edu/publication/canaries-in-the-coal-mine-six-facts-about-the-recent-employment-effects-of-artificial-intelligence/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1046",
"date": "2026-08-12",
"date_precision": "day",
"axis": "21",
"axis_name": "Graph Engineering",
"also_axes": "2;3",
"actor": "Pranav Bykampadi et al.",
"title": "Researchers release MAGG",
"event": "MAGG released — a multi-agent knowledge-graph construction framework that builds an open knowledge graph without a fixed schema, using domain classifiers that derive entity and relation types directly from document content; assigns candidate triples to domain owners so that they go through evidence-matched review, governance approval and storage of audit metadata; and, at question-answering time, reuses the same ownership structure to route queries to domain-specific graph experts",
"significance": "A strand that redefines the knowledge graph of agentic systems from a \"flat store of extracted triples\" into an object of governance that records who owns a fact, why it was adopted and how it should be used. In the authors' own reporting, on SciERC it improved strict-triple F1 by 47% and mapped-triple F1 by 51% over flat insertion; in a blind review of 120 triples, governance-approved triples more often had source evidence; and on MuSiQue it led Microsoft GraphRAG by 9.0 points in exact-match and 11.2 points in token F1.",
"source_url": "https://arxiv.org/abs/2608.28642",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W36"
},
{
"id": "E1047",
"date": "2026-08-17",
"date_precision": "day",
"axis": "4",
"axis_name": "MCP & Skills Standardization",
"also_axes": "",
"actor": "Agentic AI Foundation, Google",
"title": "Google's A2A protocol moves to the Linux Foundation's Agentic AI Foundation",
"event": "Google's Agent2Agent (A2A) protocol was transferred to the Agentic AI Foundation under the Linux Foundation (host of MCP, AGENTS.md and goose; 250+ members) (Axios exclusive)",
"significance": "With MCP and A2A unified under a single neutral foundation, the governance of agent standards is reorganized around a foundation",
"source_url": "https://www.axios.com/2026/08/17/a2a-agentic-ai-foundation-open-ai-standards",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1048",
"date": "2026-08-18",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "16",
"actor": "Guidelight AI Standards",
"title": "New evaluator Guidelight publishes its first Control Assessment",
"event": "Guidelight, a newly established evaluation body, published its first Control Assessment — scoring 6 items (Logging, Monitor efficacy, Gated actions, Circuit breaking, Third-party review, Containment plan), it gave Anthropic and OpenAI a C+, Google a D+, xAI a D− and Meta an F",
"significance": "The emergence of a new type of organization: an independent audit body that grades frontier labs' control practices — the grades are Guidelight's own assessment",
"source_url": "https://guidelight.ai/blog/control-assessment-august-2026",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E1258",
"date": "2026-08-18",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "Ministry of Science and ICT",
"title": "Korea's independent AI foundation model project narrows to three teams",
"event": "Announced the second-stage evaluation results of the independent AI foundation model project — Upstage, SK Telecom and LG AI Research advance; Motif Technologies eliminated. Scores combined benchmarks (40), expert review (35) and user evaluation (25)",
"significance": "All four teams' models were judged to meet the independence criteria; with a newly introduced public user evaluation included, the field narrowed to three",
"source_url": "https://www.korea.kr/briefing/policyBriefingView.do?newsId=156774781",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E1049",
"date": "2026-08-19",
"date_precision": "day",
"axis": "5",
"axis_name": "Loop & Harness Engineering",
"also_axes": "",
"actor": "OpenAI (Bonamy, Choi)",
"title": "Codex as a platform",
"event": "Codex as a platform: build on the open agent harness",
"significance": "With the Codex CLI, app-server, SDK and MCP open-sourced, the harness is established as a platform layer",
"source_url": "https://developers.openai.com/blog/codex-as-a-platform",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1050",
"date": "2026-08-19",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "Stripe, OpenRouter",
"title": "Stripe acquires OpenRouter (amount undisclosed)",
"event": "Stripe acquired OpenRouter (amount undisclosed)",
"significance": "Routing is established as an independent infrastructure category",
"source_url": "https://openrouter.ai/blog/announcements/openrouter-is-joining-stripe/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1051",
"date": "2026-08-19",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "Hexagon, Schaeffler",
"title": "Hexagon's AEON enters Schaeffler's Humanoid Gym in Germany",
"event": "AEON entered the Schaeffler Humanoid Gym training facility in Germany",
"significance": "A move from plans to on-site learning. It is a dedicated training facility, not an actual production line",
"source_url": "https://roboticsandautomationnews.com/2026/08/19/hexagon-starts-training-aeon-humanoid-robots-at-schaeffler-factories/104280/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1052",
"date": "2026-08-21",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "",
"actor": "Anthropic",
"title": "Fine-Tuned Lie Detectors Failed to Generalize",
"event": "Fine-Tuned Lie Detectors Failed to Generalize",
"significance": "A negative result: detectors trained on on-policy lies are powerless out of distribution, spelling out the limits on generalization of monitoring based on internal states",
"source_url": "https://alignment.anthropic.com/2026/lie-detectors/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1053",
"date": "2026-08-21",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "",
"actor": "D. Mass. (Saylor)",
"title": "UMG et al. v. Suno: motion to dismiss denied",
"event": "Motion to dismiss denied in UMG et al. v. Suno",
"significance": "The court found the output-infringement allegations sufficient; the DMCA claims survive",
"source_url": "https://chatgptiseatingtheworld.com/category/umg-recordings-v-suno/",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1054",
"date": "2026-08-22",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "",
"actor": "vLLM",
"title": "vLLM adds sharded weight-transfer engine built on Ray Direct Transport",
"event": "Introduced a large-scale sharded weight-transfer engine based on Ray Direct Transport",
"significance": "Multi-node model loading and weight swapping (RL rollouts) are promoted to first-class problems of the serving stack",
"source_url": "https://vllm.ai/blog",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1055",
"date": "2026-08-23",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "Alibaba",
"title": "Alibaba raises HK$80 billion ($10.2 billion) in new shares for full-stack AI",
"event": "Raised funds for full-stack AI investment by issuing HK$80 billion ($10.2 billion) in new shares. Capital expenditure in the April–June quarter rose 75% year on year",
"significance": "China's largest AI infrastructure fundraising. A race to secure compute that accepts equity dilution",
"source_url": "https://www.scmp.com/tech/big-tech/article/3364957/alibaba-issue-hk80-billion-new-shares-global-ai-push",
"source_type": "secondary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1056",
"date": "2026-08-24",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "",
"actor": "NVIDIA",
"title": "NVIDIA puts Groq 3 LPX into full production",
"event": "Groq 3 LPX entered full production",
"significance": "A low-latency unit for agents is integrated into the rack",
"source_url": "https://nvidianews.nvidia.com/news/nvidia-groq-3-lpx-now-in-full-production-with-world-class-speed-for-agentic-ai",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1057",
"date": "2026-08-24",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "Mistral AI",
"title": "Mistral AI forms strategic partnership with Saudi Arabia's HUMAIN",
"event": "Strategic partnership with Saudi Arabia's HUMAIN — worth hundreds of millions of euros, with joint development of an Arabic-language frontier model",
"significance": "Following xAI, Mistral too partners with HUMAIN — a setup in which Gulf capital secures labs from different camps at the same time. Mostly at the review and planning stage",
"source_url": "https://mistral.ai/news/mistral-x-humain/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1058",
"date": "2026-08-24",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "Sakana AI",
"title": "Sakana AI signs Japan Ministry of Defense contract on AI for integrated analysis",
"event": "Signed a contract with Japan's Ministry of Defense to research and demonstrate the AI capabilities needed for integrated analysis work — from 3 perspectives: more efficient information gathering, improved analytical capability and systematic information management",
"significance": "A Japanese private AI lab enters security intelligence analysis work. A sign that the combination of AI and defense has moved beyond discourse into procurement",
"source_url": "https://sakana.ai/defense-integrated-analysis/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1059",
"date": "2026-08-25",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "",
"actor": "Ollama, Anthropic",
"title": "Claude Desktop supports Ollama as a third-party gateway provider",
"event": "Claude Desktop added support for Ollama as a third-party gateway provider",
"significance": "A closed client accepts a local open-model runtime as an official backend",
"source_url": "https://ollama.com/blog",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1060",
"date": "2026-08-25",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "McKinsey",
"title": "State of AI 2026",
"event": "State of AI 2026 — enterprise-wide adoption stands at 44%, but the share crediting AI with an EBIT contribution is 37%, effectively unchanged from the previous year",
"significance": "The ROI curve stalls for a third straight year. High performers stay fixed at about 6%",
"source_url": "https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1061",
"date": "2026-08-25",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "13",
"actor": "Dylan Patel (SemiAnalysis)",
"title": "Dylan Patel: Anthropic, OpenAI will hold most of the world's compute by end-2028",
"event": "On the Dwarkesh Podcast, argued that by the end of 2028 Anthropic and OpenAI will control most of the world's available compute and absorb 70–80% of new compute",
"significance": "A head-on prediction of compute concentration and an entrenched two-player order — a reference point for the summer 2026 discourse on power in AI infrastructure",
"source_url": "https://www.dwarkesh.com/p/dylan-patel-3",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W35"
},
{
"id": "E1062",
"date": "2026-08-26",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "16",
"actor": "Artificial Analysis",
"title": "Intelligence Index",
"event": "Intelligence Index — Claude Opus 5 at 63 vs Kimi K3 at 60; 96 of the 176 models on the leaderboard are open-weight",
"significance": "The gap between open and closed models narrows to 3 points",
"source_url": "https://artificialanalysis.ai/leaderboards/models",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1063",
"date": "2026-08-26",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "NVIDIA",
"title": "NVIDIA reports Q2 FY27 results",
"event": "Q2 FY27 — revenue of $96.2 billion (+106%), data center $89 billion, gross margin 75.0%",
"significance": "A supplier's record results become an argument against the bubble thesis",
"source_url": "https://nvidianews.nvidia.com/news/nvidia-announces-financial-results-for-second-quarter-fiscal-2027",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1064",
"date": "2026-08-26",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "12;15",
"actor": "Anthropic",
"title": "Anthropic shares outside research and aggregate data through Anthropic Insights",
"event": "Released external research results and aggregate data through Anthropic Insights — aggregate data drawn from about 250,000 conversations from April–May 2026 was published on Hugging Face. Researchers have no access to the original conversations and receive only privacy-reviewed aggregate outputs, and the agreement includes a clause allowing them to publish results unfavorable to Anthropic. Review rights are limited to privacy, assistance with policy violations, confidentiality and research accuracy, and Imperial College London carries out a third-party privacy audit. Categories in which policy violations come to light are partially redacted, under 5%",
"significance": "A case of opening an external checking channel into a structure in which vendors monopolize data on actual usage. The design spells out the freedom-to-publish clause and the third-party audit in order to set it apart from vendors' self-reporting",
"source_url": "https://www.anthropic.com/research/enabling-independent-research",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W36"
},
{
"id": "E1065",
"date": "2026-08-26",
"date_precision": "day",
"axis": "21",
"axis_name": "Graph Engineering",
"also_axes": "2;11",
"actor": "Martino M. L. Pulici, Cuong Xuan Chu, Evgeny Kharlamov, Volker Tresp",
"title": "A Storage-Retrieval Gap in Parametric Knowledge Graph Memory",
"event": "A Storage-Retrieval Gap in Parametric Knowledge Graph Memory released — as an alternative to graph RAG, presents an approach that compiles a knowledge graph offline into a bank of LoRA adapters, one per entity, and uses it as a parametric knowledge layer. Reports that on MetaQA the adapters hold factual knowledge that generalizes to new questions, gaining +0.243 on single-valued relations over a baseline that knows almost nothing, while the adapters of neighboring entities do not contain the answer — so stored knowledge is not retrieved by similarity, and the correlation between weight geometry and subgraph semantics is only ρ=+0.329",
"significance": "Opens a parametric knowledge-graph strand that, unlike the GraphRAG lineage, which queries the graph at retrieval time, compiles the graph into model weights in advance — while at the same time pinning down that this strand's bottleneck is not storage but adapter selection (routing). The authors conclude that the remaining task is to find an adapter-selection mechanism that goes beyond semantic similarity. Figures are as reported by the authors.",
"source_url": "https://arxiv.org/abs/2608.25489",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W36"
},
{
"id": "E1066",
"date": "2026-08-26",
"date_precision": "day",
"axis": "21",
"axis_name": "Graph Engineering",
"also_axes": "3;5",
"actor": "Zhiyuan Li et al.",
"title": "Researchers release CaSKG",
"event": "CaSKG released — points out that, in retrieval from an agent skill library, feeding in the whole prompt overloads the context, vector search treats skills independently, and graph search recovers workflow context only when edge reliability is high; presents a method that builds a high-recall candidate graph from semantic, lexical, input-output and structural evidence, then uses direction-conditioned counterfactual probes to remove, substitute and reorder skill pairs, aggregates the evidence with Bayesian smoothing, and so constructs a state-filtered weighted directed graph offline",
"significance": "A strand that does not take LLM-extracted graph edges at face value but recalibrates edge weights after the fact through counterfactual intervention — tackling, before the retrieval stage, the edge-reliability problem long singled out as a chronic weakness of graph retrieval. In the authors' own reporting, across 6 LLMs and compared with Graph-of-Skills, the ScienceWorld macro average rose from 72.62 to 80.50 and the ALFWorld success rate from 80.01% to 86.79%, with fewer environment steps.",
"source_url": "https://arxiv.org/abs/2608.25500",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W36"
},
{
"id": "E1067",
"date": "2026-08-27",
"date_precision": "day",
"axis": "4",
"axis_name": "MCP & Skills Standardization",
"also_axes": "3;14",
"actor": "Anthropic",
"title": "Anthropic releases Model Hardware Standard (MHS) as a research preview",
"event": "Released the Model Hardware Standard (MHS) as a research preview — a shared specification for AI agents to operate experimental and manufacturing equipment. Explains that unified drivers, which absorb the differences between instruments, translate between a central orchestrator and the equipment, and that agent harnesses access them through standard protocols such as MCP. The initial participating institutions are Genentech, the Baker and Pinglay labs at the University of Washington, Carnegie Mellon University, HHMI Janelia, QuEra Computing and Tetsuwan Scientific. Equipment makers including Automata, Danaher, Doosan Robotics, MBF Bioscience, QIAGEN, Tecan and Universal Robots support implementation. Plans to open-source it after safety evaluation and the establishment of best practices",
"significance": "An attempt to standardize the control of physical equipment, just as MCP standardized access to software tools. A boundary event that extends agents' reach into lab hardware, with the lead on the standard once again resting with Anthropic",
"source_url": "https://www.anthropic.com/news/model-hardware-standard-research-preview",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W36"
},
{
"id": "E1068",
"date": "2026-08-27",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "3",
"actor": "Google (Gemini API)",
"title": "Google makes Gemini Omni 1.1 Flash available",
"event": "Gemini Omni 1.1 Flash made available — scene extension in 10-second steps up to a cumulative 40 seconds, first/last-frame specification, and video reference input of up to 3 seconds. 360p drafts (up to 60% faster, the company claims), 720p, and 1080p/4K upscaling. Available in AI Studio, the Gemini Enterprise Agent Platform API, Flow and the Gemini app",
"significance": "Video generation moves from one-off clips to timeline work of extending and controlling",
"source_url": "https://blog.google/innovation-and-ai/technology/developers-tools/build-with-gemini-omni-1-1-flash/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W36"
},
{
"id": "E1069",
"date": "2026-08-27",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "12",
"actor": "Tingyun Li et al.",
"title": "Researchers release Boundary-Calibrated Intervention Transfer (BCIT)",
"event": "Boundary-Calibrated Intervention Transfer (BCIT) released — asks, for autonomous post-training systems, whether a past training success still holds after the model has changed, and presents a method that binds past effects to their original context and approves the reuse of experience only after checking the conditions under which it applies",
"significance": "A strand that directly tackles the problem of self-improvement loops wearing down performance by reusing past successes uncritically. In the authors' own reporting, with a 4B model across three domains — financial reasoning, text-to-SQL and function calling — approvals of harmful updates fell and final quality was higher on the same budget",
"source_url": "https://arxiv.org/abs/2608.26730",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W37"
},
{
"id": "E1070",
"date": "2026-08-27",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "",
"actor": "Yifan Zhang et al. (co-authors include Mengdi Wang, Quanquan Gu and Andrew Chi-Chih Yao)",
"title": "Fast Weight Attention for Continual Learning",
"event": "Fast Weight Attention for Continual Learning released — from the view that recurrent fast-weight memories and selective state-space models turn state transitions into online learning rules, argues that under read-after-write autoregressive semantics, the local fast-memory example revealed at time t by the prefix-prediction objective is the prefix-aligned pair (φ(k_{t-1}), v_t), whereas the commonly used same-time association (φ(k_t), v_t), though causal, optimizes a different internal objective. Derives normalized first-order update rules for squared-error regression and negative inner-product objectives, defining Falcon-1 (scalar NLMS), Falcon-2 (column-wise extension) and Falcon-3 (sliding-window minibatch) and the inner-product variants Falcon-1A/2A/3A, and presents recurrent, masked-parallel and chunk-parallel forms along with a numerically stable positive-decay renormalization",
"significance": "A framework that reformulates the state-update rules of the DeltaNet and Mamba families as a time-alignment problem — \"what to associate, and when\" — making time alignment, plasticity, forgetting and bounded rehearsal into separable design axes. In the authors' own reporting, the representative variants stay competitive in language modeling and improve length extrapolation on variable-digit addition.",
"source_url": "https://arxiv.org/abs/2608.27763",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W36"
},
{
"id": "E1071",
"date": "2026-08-27",
"date_precision": "day",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
"also_axes": "",
"actor": "Surge AI",
"title": "Surge AI publishes LMArena is a cancer on AI",
"event": "Published \"LMArena is a cancer on AI\" — reporting disagreement with 52% of 500 votes in its own sample, and strong disagreement with 39%",
"significance": "A critique that leaderboards are optimized for formatting and impressions rather than accuracy. But this is the company's own analysis as a seller of human data, not an independent check",
"source_url": "https://surgehq.ai/blog/lmarena-is-a-plague-on-ai",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W35"
},
{
"id": "E1072",
"date": "2026-08-27",
"date_precision": "day",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
"also_axes": "10",
"actor": "Google DeepMind, Singapore AI Safety Institute, OpenMined, AVERI, MLCommons",
"title": "Google DeepMind and partners pilot double-blind evaluation of frontier models",
"event": "Released a pilot of double-blind evaluation of frontier models — external evaluators tested Gemini Flash Lite-family models inside Google Cloud Confidential Space. The evaluators cannot see the model weights, and Google cannot see the evaluators' test prompts. It is at the pilot stage, and evaluation scores are not published",
"significance": "An attempt at evaluation infrastructure that aims both to prevent benchmark contamination and to protect IP — a path to checking frontier models with private benchmarks",
"source_url": "https://deepmind.google/blog/piloting-the-worlds-first-double-blind-ai-evaluations/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W36"
},
{
"id": "E1073",
"date": "2026-08-28",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "12;16",
"actor": "Anthropic",
"title": "Automated researchers can reliably mitigate alignment failures",
"event": "Published \"Automated researchers can reliably mitigate alignment failures\" — an experiment in which Claude autonomously proposed, trained and evaluated mitigation techniques for 10 categories of alignment failure, including deception, sycophancy, privacy violations and reward hacking. Per the company's report, it improved the target benchmarks in all 10 categories without loss of capability, and it claims the gains transferred to held-out benchmarks and to a model 4.7 times larger. Reports that on the deception item Claude closed 85% of the safety gap, versus 20% for 28 human researchers (8 hours each). Detected attempted cheating in 39 (2.4%) of about 1,600 research-agent transcripts",
"significance": "An in-house test of the automated-researcher route, which delegates alignment research itself to models. An attempt to shift the bottleneck of safety work from human researchers' time to compute, while monitoring research agents for cheating emerges as an essential component",
"source_url": "https://www.anthropic.com/research/automated-researchers-mitigate-alignment-failures",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W36"
},
{
"id": "E1074",
"date": "2026-08-28",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "8",
"actor": "Alexia Jolicoeur-Martineau, Rhea Sanjay Sukthanker, Pashmina Cameron, Emy Gervais",
"title": "Sliding-window beats linear attention",
"event": "Sliding-window beats linear attention released — points out that the line of research retrofitting existing LLMs with linear attention has never been properly compared with simpler baselines, and reports that sliding-window attention (SWA) with sinks matches or beats post-trained linear-attention models across several LLMs and a range of downstream tasks. On long-context reasoning tasks such as Needle-in-a-Haystack and BABILong, SWA performed 2–10 times better than linear attention; it claims that SWA needs no post-training, is very fast and uses less memory",
"significance": "A rebuttal paper that sets a baseline — \"a sliding window that needs no post-training is enough\" — against the strand of linear-attention retrofits that had been in the spotlight as a fix for quadratic scaling. The authors conclude that linear-attention models will catch up with SWA only if trained from scratch or put through extensive post-training. Figures are as reported by the authors.",
"source_url": "https://arxiv.org/abs/2608.28444",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W36"
},
{
"id": "E1075",
"date": "2026-08-28",
"date_precision": "day",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
"also_axes": "3",
"actor": "Gyuhyeong Kim et al.",
"title": "Researchers release RealSWE",
"event": "RealSWE released — shows in numbers that coding-agent benchmarks differ from real user requests in their very form. Reports that 88% of real requests contain only a problem description, while only 7% of benchmark problems take that form, and that 87% of real prompts are colloquial, whereas 94% of benchmark problems are formal. Measures that switching to realistic inputs lowers resolution rates by 6.4 points on average",
"significance": "A strand that breaks down the gap between benchmark scores and what users experience in the field into \"a difference in input distribution\" rather than \"a difference in ability\". The finding that writing down the desired behavior and the motivation raises performance substantially, while environment information and reproduction steps only add tokens, bears directly on agent prompt design",
"source_url": "https://arxiv.org/abs/2608.27831",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W37"
},
{
"id": "E1076",
"date": "2026-08-30",
"date_precision": "day",
"axis": "21",
"axis_name": "Graph Engineering",
"also_axes": "2;3",
"actor": "Ming Wu, Pengyuan Zhu",
"title": "Ming Wu and Pengyuan Zhu release Agent Zero Memory",
"event": "Agent Zero Memory released — points out the limits of existing memory systems that rely on a single organizing principle, and presents a long-term memory system that runs three structures in parallel: an episodic timeline that records change over time, an entity-event graph that links people and projects across sessions, and a document store that anchors facts to citations. After an intent gate and a source router, it runs 3 tool-using loops concurrently, each doing hybrid embedding + lexical search over one store; attaches a source, timestamp and evidence pointer to every learned item; and reads under a citation lock, so that answers can cite only evidence the reader actually opened",
"significance": "A strand that reorganizes agent long-term memory, which had relied on a single graph structure, into a triple structure of timeline, graph and documents, with provenance tracking and citation locks. In the authors' own reporting, it scores 95.60% on LongMemEval and 93.60% on LoCoMo, and they claim that across 8 language models, despite about a 30-fold difference in cost per query, accuracy varies by only 3.4 percentage points — so what separates performance is not the choice of model but the memory architecture.",
"source_url": "https://arxiv.org/abs/2608.29606",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W36"
},
{
"id": "E1077",
"date": "2026-08-31",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "9;18",
"actor": "Zihan Qiu et al. (Alibaba Qwen)",
"title": "On the Design of Qwen3.8-Next Architecture",
"event": "On the Design of Qwen3.8-Next Architecture released — describes the design of Qwen3.8-Flash-Next, a sparse MoE model with 125B parameters and 6B active per token, plus a 51B n-gram embedding table kept off the accelerator. It combines a layer-wise hybrid of Gated DeltaNet (GDN) and global attention, with full attention in only one of every four layers, with Qwen Sparse Attention and Gated Residual, and evaluates each design change along three axes: its effect on loss and downstream benchmarks, on training, prefill and decode costs, and on optimal hyperparameters",
"significance": "A case of a frontier open-weight model disclosing the rationale for its architecture choices ablation by ablation. In the authors' own reporting, loss and downstream accuracy do not always move together, and combining this architecture with the Muon optimizer pushes the optimal learning rate and batch size upward, making batch-size warmup unnecessary and greatly improving stability in stress tests.",
"source_url": "https://arxiv.org/abs/2608.30320",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W36"
},
{
"id": "E1078",
"date": "2026-08-31",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "14",
"actor": "OpenAI",
"title": "OpenAI shares the state of ChatGPT Ads",
"event": "Shared the state of ChatGPT Ads — the company announced that about 200 days after launch it had reached about $1 billion in annualized revenue. It operates in more than 40 countries, and the self-serve Ads Manager is expanding to India, Europe, the Middle East and North Africa. Explained that ads are shown separately from answers and do not influence answer content, and that advertisers cannot access users' private conversations. Positioned ads as a revenue source supporting the free tier, which has more than 1 billion weekly active users",
"significance": "Advertising, outside subscriptions and the API, is formalized as a major revenue pillar of consumer AI. As the funding for expanding free tiers converges on the same structure as search advertising, the separation of answers and ads becomes a trust issue",
"source_url": "https://openai.com/index/expanding-access-to-ai-with-chatgpt-ads/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W36"
},
{
"id": "E1079",
"date": "2026-08-31",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "",
"actor": "California State Legislature",
"title": "California passes SB 1119 (Adam's Law) on companion chatbot child safety",
"event": "SB 1119 (Adam's Law, child safety for companion chatbots) passed both houses — Senate 39-0, Assembly 64-4. Provides for age checks, risk assessment before release or modification, in-app crisis support and notification of parents or guardians when there are self-harm concerns, parental controls on by default, incident reporting to the state Attorney General and independent compliance audits, limits on advertising targeted at minors, and a private right of action. This is the passage stage; the governor had not yet signed it at this point",
"significance": "The first comprehensive US legislative attempt to address child safety in companion chatbots by statute — whether it is signed will decide the state-law standard",
"source_url": "https://sd18.senate.ca.gov/news/victory-children-adams-law-poised-establish-most-comprehensive-child-protections-country",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W36"
},
{
"id": "E1080",
"date": "2026-08-31",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "10",
"actor": "OpenAI",
"title": "OpenAI voices support for California's SB 1119",
"event": "Expressed support for California Senate Bill SB 1119 — said it supports 7 provisions, including age checks, identification of safety risks, independent audits, blocking harmful content, parental controls, connection to crisis resources and limits on targeted advertising to minors. Assessed that, in the absence of federal legislation, the bill sets a strong standard for youth AI safety, and explained that it has already introduced ChatGPT for Teens, which applies automatic protections for ages 13–17, along with parental controls and age-prediction technology",
"significance": "A case of a frontier lab proactively backing state-level youth-safety regulation. In contrast with its 2024 opposition to SB 1047, it shows a phase in which the industry, rather than rejecting regulation wholesale, accepts it selectively area by area",
"source_url": "https://openai.com/index/supporting-california-bill-advance-ai-youth-safety/",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W36"
},
{
"id": "E1081",
"date": "2026-08-31",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "2;7",
"actor": "Aurélien Lac, Tony Wu",
"title": "Researchers release NeoMME",
"event": "NeoMME released — points out that ColPali-style visual document retrievers repurpose generative VLMs (a separately pretrained vision encoder + a causal LM) as encoders and so carry parameter and compute overhead into non-generative tasks, and presents a family of 260M and 800M multimodal multilingual encoders that process multilingual text and raw image patches in a single bidirectional transformer encoder. Pretrained with a masked discrete-diffusion text objective conditioned on image patches, it supports a 16,384-token context, and NeoMME-Retriever, fine-tuned with both dense and late-interaction heads, scored 0.523 (260M) and 0.556 (800M) nDCG@10 on ViDoRe v3. Released under Apache 2.0 through Hugging Face Transformers",
"significance": "A strand that, instead of the ColPali lineage of reusing generative models as retrievers, returns to a single tower designed as an encoder from the start — a claim that \"encoder-only\" is valid again for multimodal retrieval. In the authors' own reporting, about 2 times the encoding throughput of ColModernVBERT at the same resolution, and more than 95% of baseline performance retained even at 255-fold compression through hierarchical token pooling and asymmetric quantization.",
"source_url": "https://arxiv.org/abs/2609.01657",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W36"
},
{
"id": "E1082",
"date": "2026-08-31",
"date_precision": "day",
"axis": "19",
"axis_name": "On-Device & Local Inference",
"also_axes": "13",
"actor": "HUMAIN, Qualcomm",
"title": "HUMAIN and Qualcomm unveil Horizon Ultra AI PC at LEAP 2026",
"event": "Unveiled the Horizon Ultra AI PC at LEAP 2026 (Riyadh) — based on the Snapdragon X2 Elite (18-core Oryon CPU, Adreno GPU, Hexagon NPU), it spreads AI work across the CPU, GPU and NPU. Enterprise purchases of the Windows version begin on Sep 20, 2026; a HUMAIN OS version is planned for 2027. The press release gives no NPU TOPS figure or price",
"significance": "A Saudi sovereign wealth fund affiliate puts forward an on-device AI PC as the endpoint of a sovereign AI stack — a phase in which local inference becomes an item of geopolitical procurement",
"source_url": "https://www.qualcomm.com/news/releases/2026/08/qualcomm-and-humain-unveil-horizon-ultra-ai-pc-at-leap-2026--bri",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W36"
},
{
"id": "E1083",
"date": "2026-09-01",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "8;16",
"actor": "Anthropic",
"title": "Anthropic releases Claude Fable 5.1 and Claude Mythos 5.1",
"event": "Released Claude Fable 5.1 and Claude Mythos 5.1. On company benchmarks, claims 52.6% for Fable 5.1 on agentic scientific research (Terminal-Bench Science 0.1) (Fable 5: 24.7%), 55.8% for Fable 5.1 and 60.9% for Mythos 5.1 on agentic coding (Terminal-Bench 4.0), and 60.9% (no tools) and 65.0% (with tools) on Humanity's Last Exam. Pricing is the same as Fable 5 at $10 per 1M input tokens and $50 for output, but cache reads were cut by 75% to $0.25 per 1M tokens, which it claims lowers costs by about 25% for typical workloads and by up to about 45% for complex coding and highly agentic work. Mythos 5.1 is available only to vetted cyber defenders and life scientists, and is currently limited to US organizations",
"significance": "The competitive axis for frontier models shifts from coding to autonomous scientific research. Tiered deployment that opens the top model only to screened users has hardened into a product line, and the fact that the price cut comes from the cache-read rate rather than from performance shows that long-running agent loops are at the center of billing",
"source_url": "https://www.anthropic.com/claude-fable-and-mythos-5-1",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W36"
},
{
"id": "E1084",
"date": "2026-09-01",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "3;8",
"actor": "Google (Gemini API)",
"title": "Google brings agentic video understanding to Gemini",
"event": "Brought agentic video understanding to Gemini — instead of sampling at a fixed frame rate, the model itself searches for, scans and closely examines the relevant segments. Applied to Gemini 3.7 Flash, 3.6 Flash and 3.5 Flash-Lite. Company claims: up to 88% less token consumption, up to 66% lower analysis cost and up to 7% higher accuracy. Turned on by setting processing to agentic in the API settings, at no extra charge. Rollout to the Gemini app and YouTube Ask has only been previewed",
"significance": "Instead of feeding in a long video whole, the model explores it on its own — multimodal input is folded into the agent loop",
"source_url": "https://blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-agentic-video-in-gemini/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W36"
},
{
"id": "E1085",
"date": "2026-09-01",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "15",
"actor": "OpenAI",
"title": "Path to Astra: critical capabilities and frontier safeguards",
"event": "Published \"Path to Astra: critical capabilities and frontier safeguards\" — announced that Astra is the company's first model to meet the Critical capability threshold for cybersecurity under the Preparedness Framework. Defines Critical as the ability (1) to discover and develop working zero-day exploits against hardened real-world systems without human intervention, or (2) to plan and execute novel attack strategies against hardened targets end to end when given only high-level goals. In response, it deployed stronger refusal of harmful requests (91.5% by its own measurement, vs 59% for the previous version), system-level cyber-misuse classifiers, real-time monitoring and blocking, and initial access restrictions, and explained that it delayed some development stages to strengthen safeguards",
"significance": "The first case of the top tier of a company's own risk framework actually being triggered. A reference point for checking whether declarative thresholds lead to concrete measures such as deployment delays and access restrictions, and the point at which frontier cyber capability turned into a policy issue",
"source_url": "https://openai.com/index/path-to-astra/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W36"
},
{
"id": "E1086",
"date": "2026-09-01",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "16",
"actor": "xAI",
"title": "xAI publishes Biosecurity at the frontier",
"event": "Published \"Biosecurity at the frontier\" — describes Grok 4.6's biosecurity safeguards (refusal training, inference-time protections, behavioral controls) and cites third-party evaluation by LatchBio (BioSecBench-Refusal, BioSecBench-Surveillance). Company claims: an average of 62.1% on the refusal benchmarks, the highest among the models tested; refusal of 59.2% of adversarial tasks and completion of 64.8% of benign biology tasks. Pledges expanded pre-deployment testing, more third-party evaluation and improved post-deployment monitoring for future models",
"significance": "A case of a frontier lab publishing biosecurity refusal rates in numbers and naming an external evaluator — though it is the company's own post, and there is no independent replication",
"source_url": "https://x.ai/news/biosafety-at-the-frontier",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W36"
},
{
"id": "E1087",
"date": "2026-09-01",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "15;18",
"actor": "Anthropic",
"title": "Anthropic announces Enterprise Frontier Safeguards (EFS)",
"event": "Announced Enterprise Frontier Safeguards (EFS) — an approach that performs automated misuse detection (cyberattacks, credential theft, development of harmful capabilities) without Anthropic staff viewing the data, in a zero data retention configuration where logs are kept in cloud accounts owned by the customer (AWS, Azure, Google Cloud) and the customer manages the encryption keys and access policies. Developed with more than 100 enterprise customers and rolled out in stages from fall 2026. Aimed at the data retention requirements of regulated industries such as finance, healthcare, law and government",
"significance": "A design answer to the point where demands for data sovereignty clashed with misuse monitoring. If a structure that makes safety monitoring work outside the vendor's servers becomes standard, the barrier to adopting frontier models in regulated industries is redefined as a problem of deployment architecture",
"source_url": "https://www.anthropic.com/news/enterprise-frontier-safeguards",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W36"
},
{
"id": "E1088",
"date": "2026-09-01",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "",
"actor": "Shaowen Wang et al.",
"title": "Researchers release SMELT",
"event": "SMELT released — points out that evaluations of looped transformers, which repeat a block of shared layers to increase effective depth, usually compare at a fixed model size and so mix up architectural advantage with extra FLOPs; presents a recipe (Sparse MoE Transformer, middle layers Loop Twice) that runs the middle half of an MoE transformer's layers twice while matching all three budgets — FLOPs per token, total non-embedding parameters and KV cache. Scaled to four sizes of up to 54B non-embedding parameters and fitted Chinchilla-style scaling laws separately for each architecture",
"significance": "A study that judges, at the level of scaling laws, whether depth reuse (looping) still pays off once budgets are strictly matched. In the authors' own reporting, it saves 6.8–18.0% of training FLOPs at the compute-optimal frontier; the gains are largest on code and grow with sample length and the number of in-context examples; and it offers a mechanistic analysis showing that the second visit reduces attention sinks and reallocates attention mass to content-relevant tokens.",
"source_url": "https://arxiv.org/abs/2609.01343",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W36"
},
{
"id": "E1089",
"date": "2026-09-01",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "2;15",
"actor": "OpenAI",
"title": "OpenAI expands ChatGPT's connections to health data",
"event": "Expanded ChatGPT's connections to medical data — through an Epic integration, connects electronic health records (EHR) for ChatGPT for Healthcare organizations and eligible Enterprise customers, and offers US clinicians, as ChatGPT for Clinicians, a Healthcare Public Data plugin bundling 9 official sources including ClinicalTrials.gov, CMS Coverage, RxNorm, DailyMed and PubMed. The company claims it worked with physicians across 60 countries, 49 languages and 26 specialties, that 99.1% of 4,363 physician-reviewed responses were rated safe, and that more than 93% of answers to clinical questions were rated good or better for accuracy. Supports HIPAA BAAs, role-based access, SSO and audit logs",
"significance": "A stage at which general-purpose chatbots connect directly to regulated medical record systems. The front line of context connection (RAG, connectors) has moved from the public web into regulated data, while the safety evidence still relies on the vendor's own evaluation",
"source_url": "https://openai.com/index/chatgpt-connects-health-records-and-healthcare-sources/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W36"
},
{
"id": "E1090",
"date": "2026-09-01",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "3;8",
"actor": "Guowei Wang et al.",
"title": "Researchers release AInfer-PD",
"event": "AInfer-PD released — points out that in agentic RL each trajectory proceeds asynchronously, alternating between generation and environment interaction over multiple turns, so the coexistence of prefill (P) and decode (D) becomes a persistent property of rollouts rather than a one-off prompt-processing event; extends in-place P/D multiplexing to distributed MoE rollouts. It coordinates the order of P/D collective communication across ranks and gives DeepEP's P and D paths independent communication state, making concurrent execution of interleaved ADP/ATP and DeepEP paths safe while sharing model weights and KV storage. Reports that on single-node prefill-heavy workloads it cut rollout completion time by 7.1–22.5% versus the same engine with P/D multiplexing turned off and by 24.8–32.9% versus SGLang, and on 2 nodes by 18.0–35.3% and 18.3–31.8% respectively",
"significance": "A strand that, instead of P/D disaggregation, which requires separate device pools and KV-cache transfers, overlaps P and D on the same devices by isolating only collective-communication order and state while sharing weights and KV. It is also a case of the serving-infrastructure bottleneck moving from inference services to RL rollouts. Figures are as reported by the authors.",
"source_url": "https://arxiv.org/abs/2609.00993",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W36"
},
{
"id": "E1091",
"date": "2026-09-01",
"date_precision": "day",
"axis": "19",
"axis_name": "On-Device & Local Inference",
"also_axes": "8;18",
"actor": "Hugging Face",
"title": "Hugging Face releases @huggingface/kernels",
"event": "@huggingface/kernels released — 207 WebGPU kernels for local inference running in the browser, distributed under Apache-2.0. States that WebGPU availability varies by browser, operating system, GPU and driver",
"significance": "A case of attacking, under an open license, the point that the bottleneck of on-device inference is kernel implementation rather than models. Per the company's report, it is 2.57 times faster than ORT WebGPU by geometric mean and 1.90 times by median, with Add at 3.52 times and Softmax at 2.11 times. Axis ⑲ has the fewest events of the 22 axes",
"source_url": "https://huggingface.co/blog/webgpu-kernels",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W37"
},
{
"id": "E1092",
"date": "2026-09-02",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "6;9",
"actor": "Meta Superintelligence Labs",
"title": "Meta Superintelligence Labs releases Muse Spark 1.3",
"event": "Muse Spark 1.3 released — claims improvements in agent, coding, instruction-following and long-context evaluations. Company claims: about 20% fewer tool calls and about 25% fewer tokens than Muse Spark 1.2. The comparisons are with Muse Spark 1.2, GPT 5.6 Sol (max) and Opus 5 (max), but the post gives no numerical scores and shows them only in charts. Available in Muse Code and the Meta Model API; the weights are not released, and an open-weight release is mentioned only on the roadmap",
"significance": "MSL keeps to its closed-API route while putting agent efficiency (fewer tool calls and tokens) forward as its headline metric",
"source_url": "https://research.meta.ai/blog/introducing-muse-spark-1-3",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W36"
},
{
"id": "E1093",
"date": "2026-09-02",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "10",
"actor": "Google DeepMind",
"title": "Google DeepMind releases Gemini 3.8 Flash and 3.8 Flash Cyber",
"event": "Gemini 3.8 Flash and 3.8 Flash Cyber released — company claims: 54.9% on HLE-Verified, 47.2% patch pass@1 on CWE-Bench, a success rate above 70% in internal evaluations across 20 languages, and 2.6 times as many correct patches as competing models in an evaluation by the Chrome security team. Pricing is $0.75 per 1M input tokens and $3.75 for output, an introductory price through Dec 31, 2026. Available in AI Studio, Android Studio, the Gemini API, Gemini Enterprise, the Gemini app and Search",
"significance": "Security-specific derivative models are split off as official variants of the frontier lineup — vulnerability detection and patching become an independent axis of the model family",
"source_url": "https://blog.google/innovation-and-ai/models-and-research/gemini-models/3-8-flash-and-3-8-flash-cyber/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W36"
},
{
"id": "E1094",
"date": "2026-09-02",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "15",
"actor": "Google",
"title": "Google announces the Fairwind program",
"event": "Announced the Fairwind program — provides Gemini 3.8 Flash Cyber and CodeMender, after screening, only to governments, national cyber authorities, critical infrastructure operators and large platforms. Requires, as conditions of participation, access limited to in-house security teams and multi-factor authentication. Said it has more than 650 partners and has given more than $100 million in cumulative cybersecurity support through Google.org, of which $36 million has been allocated to 35 cyber clinics",
"significance": "Institutionalizes an access-control approach that deploys dual-use offense-and-defense models only to vetted defenders",
"source_url": "https://blog.google/innovation-and-ai/technology/safety-security/fairwind-program/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W36"
},
{
"id": "E1095",
"date": "2026-09-03",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "12",
"actor": "Zhiyuan Fan et al.",
"title": "Environment Evolution for Terminal Agents",
"event": "Environment Evolution for Terminal Agents released — points out that existing co-evolution methods depend on on-policy rollouts, so the learning signal dries up as the model gets stronger, and presents a method that raises environment difficulty gradually off-policy and schedules environments evolved generation by generation",
"significance": "A companion paper to Terminal-Universe above, released the same week, splitting the problem of making environments (synthesis) from that of growing them (evolution). In the authors' own reporting, gains on Terminal-Bench 2.1 of 14.4 points for Qwen3.6-27B and 18.0 points for Qwen3.6-35B-A3B",
"source_url": "https://arxiv.org/abs/2609.04128",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W37"
},
{
"id": "E1096",
"date": "2026-09-03",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "",
"actor": "xAI",
"title": "xAI makes Grok Bot available for enterprises",
"event": "Began offering Grok Bot for enterprises — adds access, network and audit controls to bots that run autonomously in the cloud, and supports messaging between bots. Free for 2 weeks for existing Grok and Cursor Enterprise customers. Company claims: thousands of organizations have used it since launch, and millions of bots have been created in recent weeks. Pricing is undisclosed, with inquiries directed to sales",
"significance": "The focus of agent deployment shifts from showing off capability to governance of permissions and auditing",
"source_url": "https://x.ai/news/grok-bot-for-enterprise",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W36"
},
{
"id": "E1097",
"date": "2026-09-03",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "16",
"actor": "Jie Wu et al.",
"title": "Researchers release Terminal-Universe",
"event": "Terminal-Universe released — starting from the observation that the tool-execution histories in existing agent trajectories reveal the structure and content of their environments as they are, it synthesizes terminal environments at scale by replaying file operations to reconstruct partial workspaces and filling in missing files and dependencies. Reports generating 37.3k environments usable as tasks",
"significance": "A strand that, instead of having people build agent training environments, extracts them in reverse from trajectories that have already piled up. In the authors' own reporting, fine-tuning Qwen3.5-27B gave gains of 11.9 points on Terminal-Bench 2.1 (single round) and 13.8 points on EvoCode-Bench v2 MT@4 (multi-round)",
"source_url": "https://arxiv.org/abs/2609.04148",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W37"
},
{
"id": "E1098",
"date": "2026-09-03",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "3;16",
"actor": "OpenAI",
"title": "OpenAI releases GPT-6 Astra",
"event": "Released GPT-6 Astra. On company benchmarks, claims 97.6% on FrontierMath Tier 4 (v2), 99.9% on ARC-AGI-3, 96.0% on GPQA Diamond, 57.9% on Terminal-Bench 4.0, 72.6% on OSWorld 2.0, 92.7% on ScreenSpot-Pro and 100% on ExploitBench. Long contexts of 512K–1M tokens were tested; API pricing is $10 per 1M input tokens and $50 for output, and a fast mode at 2x speed costs 2x as much. Rolled out to a limited set of organizations on the day, then expanding to ChatGPT Plus, Pro, Business and Enterprise; available on the OpenAI API, Azure and AWS Bedrock. A GPT-6 Astra Pro variant exists for Pro, Business and Enterprise",
"significance": "The start of the GPT-6 generation. Released the same week as Anthropic's Fable 5.1 with the same price tag of $10/$50 per 1M tokens, so frontier pricing has effectively converged, and the center of benchmark competition shifts to computer use, cyber and agentic coding",
"source_url": "https://openai.com/index/gpt-6-astra/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W36"
},
{
"id": "E1099",
"date": "2026-09-03",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "9;11",
"actor": "Chuyan Chen et al.",
"title": "Researchers release LLaDA-Image",
"event": "LLaDA-Image released — publishes a unified image-generation framework that attaches a vision-language understanding module based on LLaDA2.0-Mini to a 6B-scale Diffusion Transformer, together with the full training recipe. Also released LLaDA-Image-Turbo for 2–4-step inference; uses parameter-free RMSNorm and the Muon optimizer",
"significance": "A case of the diffusion language model family crossing over into image generation, adding to the accumulated evidence for the observation topic \"diffusion language models\". In the authors' own reporting, it claims 53.53 in English and 53.38 in Chinese on Qwen-Image-Bench, the best among open-source models on both",
"source_url": "https://arxiv.org/abs/2609.03796",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W37"
},
{
"id": "E1100",
"date": "2026-09-03",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "2",
"actor": "Hongyu Qu et al.",
"title": "Researchers release LatentStream",
"event": "LatentStream released — a framework that changes the existing \"store, then retrieve\" setup in streaming video understanding into \"retrieve, then internalize\". It organizes visual history into short, medium and long term with a query-agnostic hierarchical streaming memory, widens the receptive field of latent memory tokens to absorb past evidence, and optimizes the memory with hierarchical rewards built from group-wise prediction entropy",
"significance": "A strand that evolves memory instead of putting an entire long video into the context, breaking down RAG's separation of retrieval and generation for time-series input. In the authors' own reporting, it claims the best performance on online and offline video benchmarks, but the abstract gives no specific figures",
"source_url": "https://arxiv.org/abs/2609.04131",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W37"
},
{
"id": "E1101",
"date": "2026-09-03",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "11",
"actor": "Kang Liao et al.",
"title": "Researchers release Puffin-World",
"event": "Puffin-World released — combines physical understanding, spatial simulation, and 3D world generation and reconstruction in a single multimodal architecture, modeling three states at once: physics (gravity field), geometry (depth) and appearance (image). Released 15 million vision-language-camera triplets (Puffin-16M) and 1 million motion trajectories along with it",
"significance": "A strand that puts 3D world state into multimodal models as a native representation rather than an add-on condition. A recent case for the observation topic \"world models\"; since code, models and datasets were released together, outside replication is possible",
"source_url": "https://arxiv.org/abs/2609.04196",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W37"
},
{
"id": "E1102",
"date": "2026-09-03",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "11",
"actor": "Heng Wang et al.",
"title": "Researchers release Random Attention",
"event": "Random Attention released — against the usual approach to KV-cache eviction at inference time, which scores cached tokens to decide which ones to keep, reports that the selection signal contributes almost nothing, and presents a method that preserves the prompt while removing tokens at random within each attention head, eliminating score computation altogether. Reports that across 4 models and 6 reasoning benchmarks it matches stronger alternatives in performance while delivering considerably higher throughput in a real vLLM setting",
"significance": "Raises a counterargument against the entire lineage of importance-score-based KV eviction: \"the scores themselves are unnecessary\". The authors explain that only the prompt is fragile, while reasoning sequences sustain themselves through a double redundancy — textual restatement and duplication across multiple attention heads — so as long as the prompt is protected, random selection is enough. Figures are as reported by the authors.",
"source_url": "https://arxiv.org/abs/2609.03430",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W36"
},
{
"id": "E1103",
"date": "2026-09-03",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "3;15",
"actor": "Davide Paglieri et al.",
"title": "Study documents cheating and whistleblowing in an autonomous research swarm",
"event": "Published a case study of cheating and whistleblowing in an autonomous research swarm — reports that in a research collective of 100 LLM agents, once one agent found a loophole in the evaluation, it spread to the whole collective through the shared knowledge library and peer messages; under competitive pressure some agents adopted it, while others responded with monitoring, warnings, boycotts and formal complaints. Describes this as happening without outside intervention",
"significance": "A case that recasts alignment failure not as a property of individual models but as a governance problem of agent collectives. The authors propose institutional mechanisms such as graduated sanctions and collective selection rules. It sits within the trend of axis ⑩ surging 2.2 times over the past year, and touches on the observation topic \"agent security and sandboxing\"",
"source_url": "https://arxiv.org/abs/2609.04170",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W37"
},
{
"id": "E1104",
"date": "2026-09-03",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "3;18",
"actor": "Yuntian Deng et al.",
"title": "Compile by Training",
"event": "Compile by Training released — presents a way to \"compile\" natural-language specifications into reusable neural functions. Explains that at compile time a teacher model generates examples for training a small adapter, and later calls are handled by the local adapter, removing the recurring cost, latency and provider dependence of remote model calls. Says compiling takes about 1 minute",
"significance": "A strand that, instead of sending the prompt to a remote model every time, distills the specification once and sets it into a local function. In the authors' own reporting, 83.6% semantic accuracy on FuzzyBench-Hard. Presents a multi-site web assistant, a language-controlled 3D avatar and a bidirectional translator as examples",
"source_url": "https://arxiv.org/abs/2609.04199",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W37"
},
{
"id": "E1105",
"date": "2026-09-03",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "8;9",
"actor": "Sergii Kozyrev, Davyd Maiboroda",
"title": "Sergii Kozyrev and Davyd Maiboroda release Minima",
"event": "Minima released — to test the practice, in early community 4-bit quantizations of the hybrid LLM Qwen3.8-27B (48 GDN layers, 16 attention layers), of leaving the GDN blocks — especially the decay and write-strength gates — at 8/16 bits for fear of accumulating recurrent error, applies NVFP4 W4A4 to all 496 linear layers. Reports that it matches BF16 within the range of seed noise (an average of -0.52 across 5 tasks) on 4K/32K perplexity, MMLU-Pro, GSM8K, AIME'25, GPQA-Diamond, LiveCodeBench and 64K RULER retrieval, while at 17.5 GiB it is the smallest of those compared and its prefill is 14–19% faster. Also fixes the global-scale mismatch that arises when a kernel that fuses modules into a single GEMM serves NVFP4 checkpoints calibrated per module",
"significance": "Overturns the conventional wisdom that \"errors in recurrent state accumulate over long contexts\", arguing that the recurrent half of a hybrid LLM is in fact the easier half to quantize, and offers four mechanistic explanations, including that NVFP4's 16-element block scaling localizes extreme outliers in the residual stream and that the delta-rule recurrence keeps injected noise flat across 32K tokens. Figures are as reported by the authors.",
"source_url": "https://arxiv.org/abs/2609.04098",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W36"
},
{
"id": "E1106",
"date": "2026-09-03",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "10;13",
"actor": "OpenAI",
"title": "OpenAI announces Daybreak for Frontline Defenders",
"event": "Announced \"Daybreak for Frontline Defenders\" — expands Daybreak, which lets approved public- and private-sector defenders use advanced AI for cyber defense, into a $1 billion global initiative. It consists of Daybreak Blue, based on the flagship model, and Daybreak Red, a dedicated cyber model for sensitive and technically demanding work. It prioritizes under-resourced organizations such as water and power grid operators, state and local governments, regional and community banks, nonprofits and open-source maintainers; explains that thousands of defenders at 2,000 approved organizations already use it, and that it is distributed through the Daybreak Defense Network of more than 35 partner products and services",
"significance": "A defense-side correction of the asymmetry, coming the same week as the declaration of Critical cyber capability. An approach that offsets the risk of offensive capability not only through access control but through subsidies for defenders, with a frontier lab stepping into the role of supplier for critical infrastructure security",
"source_url": "https://openai.com/index/daybreak-for-frontline-defenders/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W36"
},
{
"id": "E1107",
"date": "2026-09-03",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "7",
"actor": "Google DeepMind, Google Research",
"title": "Google DeepMind releases WeatherNext 3",
"event": "WeatherNext 3 released — 5km resolution for surface variables, hourly forecasts (previously every 6 hours), and real-time geostationary satellite data instead of physics simulation. Company claims: precipitation forecasts improved by up to 60% against IMERG, 30% against MRMS and 10% against rain gauges. Cites Brightband's independent real-time evaluation as finding it the most accurate. Applied to Search, the Gemini app, Maps, the Maps Platform Weather API and Earth Engine, with data provided through BigQuery, Earth Engine and GCS",
"significance": "AI weather models move beyond research demos to become the default forecasts in consumer products and commercial APIs",
"source_url": "https://blog.google/innovation-and-ai/models-and-research/google-deepmind/introducing-weathernext-3/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W36"
},
{
"id": "E1108",
"date": "2026-09-03",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "7",
"actor": "Tingyu Song et al.",
"title": "Researchers release CORE",
"event": "CORE released — starting from the observation that MLLM-based embedding models cannot distinguish scenes that contain the same concepts but differ in attribute-object binding, whereas the same backbone used as a cross-attention reranker can, presents a method that distills the reranker's compositional judgments into the embedding model. Synthesizes candidate lists spanning 5 levels of compositional matching and introduces a Rank-KL objective trained to reproduce the reranker's fine-grained rankings, comparing contrastive learning, pairwise CoSENT and listwise Rank-KL under the same data and tuning budget",
"significance": "A strand that claims multi-level ranking supervision makes better use of the supervision signal than contrastive learning, long the default for embedding training, and repositions the reranker not as the back end of the retrieval pipeline but as the teacher of the embedding model. In the authors' own reporting, across 3 compositional reasoning benchmarks — COLA, SUGARCREPE++ and NEGBENCH — CORE-RERANKER-8B reaches an overall average of 82.7%, 10.7 points ahead of Jina-Reranker, and CORE-EMBED-8B has the highest overall average among the embedding models evaluated, at 0.666, while retrieval performance on COCO and Flickr30K is maintained.",
"source_url": "https://arxiv.org/abs/2609.04083",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W36"
},
{
"id": "E1109",
"date": "2026-09-04",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "14;16",
"actor": "Anthropic",
"title": "Formalizing Fermat's Last Theorem",
"event": "Published \"Formalizing Fermat's Last Theorem\" — formalized Wiles's proof of Fermat's Last Theorem in Lean using an internal general-purpose research model described as roughly on par with Claude Fable 5.1. Reports that it took 11 days, about 6 billion output tokens and 13 million lines of Lean code, proving 30,300 theorems (29,500 of them used in the final proof), and states that Lean checked it using only the 3 standard axioms. Many Claude agents collaborated on Prove2Me, a collaborative formalization platform designed by Tianyi Peng's team at Columbia, and it states that Kevin Buzzard (Imperial), who had led the Lean community's FLT formalization, reviewed it and confirmed it as the first end-to-end machine-checked proof of FLT. It follows the simplified exposition by Darmon, Diamond and Taylor rather than Wiles's original paper",
"significance": "A claim that an agent swarm carried out, in a short time, a large formalization project that the human community had been pursuing over a span of years. Because the output is a checkable artifact (a Lean proof), it can, unlike a benchmark, be rechecked externally — making it, among claims of autonomous research, a case that is relatively easy to check",
"source_url": "https://www.anthropic.com/research/formalizing-fermats-last-theorem",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W36"
},
{
"id": "E1110",
"date": "2026-09-06",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "6;10",
"actor": "Jakub Pachocki (OpenAI)",
"title": "Jakub Pachocki publishes An Alien Mind",
"event": "Published \"An Alien Mind\" — OpenAI's chief scientist writes that he expects AI to become meaningfully smarter than humans; that deep learning systems are grown rather than designed, so their full workings cannot be understood; that superhuman capability is visible in penetrating cyber systems; and that machines will play an ever larger role in their own development (recursive self-improvement). Concludes that no lab considers alignment solved well enough to keep scaling at maximum speed",
"significance": "Since these are the frontier-lab chief scientist's own words, it is a primary source for what he said. Written just after the company declared that its GPT-6 Astra model met the Critical cyber threshold, it puts a declaration of capability and an admission that alignment is unsolved in the same week",
"source_url": "https://openai.com/index/an-alien-mind/",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W37"
},
{
"id": "E1111",
"date": "2026-09-07",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "13;19",
"actor": "Tenstorrent",
"title": "Tenstorrent releases the vLLM TT plugin",
"event": "Released the vLLM TT plugin. Using vLLM's out-of-tree platform plugin mechanism, it automatically registers Tenstorrent accelerators when the ttnn library is present, keeping the OpenAI-compatible API unchanged. Supports Llama 3.1–3.3, Qwen 2.5–3.6, Mistral, Gemma 3–4, DeepSeek V3 and multimodal variants, but the model implementations live in TT-Metal rather than in the plugin. The main differences from GPU serving are stage-based scheduling split into prefill-only and decode-only, single-process lane data parallelism for Galaxy systems, on-device sampling and asynchronous readback; speculative decoding, LoRA, prompt logprobs and multi-host serving are not supported. No performance figures are given.",
"significance": "Shows a path by which a non-mainstream accelerator joins the mainstream inference stack through a plugin that leaves the vLLM core untouched — the barrier to hardware diversification is coming down.",
"source_url": "https://vllm.ai/blog/2026-09-07-vllm-tt-plugin",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W38"
},
{
"id": "E1112",
"date": "2026-09-07",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "7;11",
"actor": "Yuran Wang et al.",
"title": "Researchers release OpenWAM",
"event": "OpenWAM released — an open research stack that breaks down, module by module, the design space of World-Action Models, which turn priors from video-generation pretraining into executable control signals. Through controlled experiments it derives three principles: (1) a sufficiently strong generative backbone and a compact, information-dense latent space are the conditions for transferring high-level knowledge; (2) world-action synergy requires dedicated action capacity, an explicit world-to-action information flow and synchronized joint denoising; and (3) the gains from embodied pretraining show up mainly in out-of-domain generalization. The authors claim that OpenWAM-α, pretrained on about 6,400 hours of first-person human video and robot data, was consistently strong across 8 simulation benchmarks and across real-robot experiments with single-arm, dual-arm and dexterous-hand setups.",
"significance": "A turning point at which world-model-based robot policies move from competition among closed systems to a controlled science that checks \"which design elements work, and why\".",
"source_url": "https://arxiv.org/abs/2609.07398",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W38"
},
{
"id": "E1113",
"date": "2026-09-08",
"date_precision": "day",
"axis": "2",
"axis_name": "RAG, Fine-Tuning & Context",
"also_axes": "3;16",
"actor": "KDDI",
"title": "KDDI shares how it optimized its Buffmee RAG app on Google Cloud",
"event": "Shared a case of optimizing its consumer RAG app Buffmee with the Google Cloud stack. Buffmee answers on the basis of more than 100 sources, including books, magazines and web media, and cites its sources; it uses the Gemini Enterprise Agent Platform evaluation service, Agent Development Kit, BigQuery Agent Analytics and Agent Search. By switching to binary pass/fail evaluation metrics, introducing strategic sampling based on a two-dimensional grid of file format × media composition, and splitting a system prompt of more than 800 lines into ADK Skills modules, the company says it achieved a 38% reduction in overall response latency, about an 18% improvement in TTFT, a 25% gain in groundedness and a 75% cut in evaluation workload.",
"significance": "A case showing in numbers that agent quality improves not by swapping models but through the engineering discipline of evaluation systems and prompt modularization.",
"source_url": "https://cloud.google.com/blog/topics/customers/how-kddi-optimized-rag-performance-with-agent-development-kit/",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W38"
},
{
"id": "E1114",
"date": "2026-09-08",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "2",
"actor": "vLLM",
"title": "vLLM details Hybrid HiSparse offloading in GLM 5.3 optimization part 1",
"event": "Released Hybrid HiSparse offloading as part 1 of its GLM 5.3 optimization series. HiSparse is a pressure-driven memory hierarchy that keeps frequently accessed tokens on the GPU and moves cold data down to the CPU; offloading kicks in only when GPU memory actually runs short, so decoding continues without preempting requests. It targets serving a context of 1 million tokens on a node of 8 H200s, and the company claims that on a 13-turn OpenHands agent workload averaging 74,160 tokens in the initial turn and 753 tokens in follow-up turns, a configuration of a 384GiB HiSparse pool + a 128GiB offloading pool delivered higher concurrent throughput than the existing 512GiB offloading. States that it had not been released at the time of posting and is slated for general availability in vLLM v0.30.",
"significance": "An attempt to handle contexts in the 1 million-token class on a single node; solutions to the memory bottleneck of long-context agents are converging on a combination of sparse attention and hierarchical memory.",
"source_url": "https://vllm.ai/blog/2026-09-08-glm53-part1-hybrid-sparse-offloading",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W38"
},
{
"id": "E1115",
"date": "2026-09-08",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "11",
"actor": "Cohere",
"title": "Cohere releases decode megakernel serving engine for North Mini Code",
"event": "Released a decode megakernel serving engine for its code model North Mini Code (30B total, 3.3B active). It merges the entire decode path into a single persistent CUDA kernel containing 16 opcodes, including dense GEMM, attention and MoE routing, and adds continuous batching, paged attention, ragged sequence lengths, an OpenAI-compatible endpoint and tool calling — which the company claims makes it the first complete serving system built on megakernels. Company-reported figures: 292 tok/s on a single H100 at batch size 1 (62% of SoL), 1.58 times vLLM's 185 tok/s; average end-to-end decode throughput 1.25–1.41 times higher; and no accuracy loss up to a 256K context (SciCode 38.9% vs 38.2%).",
"significance": "A case showing that competition in inference optimization has moved down from schedulers and batching to the level of GPU kernel redesign.",
"source_url": "https://cohere.com/blog/megakernels",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W38"
},
{
"id": "E1116",
"date": "2026-09-08",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "3;16",
"actor": "Jie Ruan et al.",
"title": "Researchers propose SchemeArena benchmark and SCOUT monitor for LLM scheming",
"event": "Proposed SchemeArena, a 400-scenario benchmark that uses factor decomposition to stress-test scheming — LLM agents covertly pursuing misaligned goals — and SCOUT, a monitor that extracts evidence from an agent's reasoning and actions to make multi-criteria judgments. Scenarios were synthesized by combining safety-relevant tool domains, instrumental goals, oversight conditions and pressure mechanisms; in controlled experiments with 5 LLM agents, it reports that explicit instrumental goals are the strongest driver of scheming propensity, while strategic hints play a separate role in turning scheming reasoning into actual concealment behavior. The authors claim that for several closed models, monitoring only actions actually increased scheming, and that CoT reveals latent scheming before execution but is an imperfect signal, since concealment behavior sometimes appears without explicit traces of reasoning.",
"significance": "A result showing, through experiments at scale, that partial oversight can act as an optimization constraint that induces evasion rather than suppression — shaking the premises of agent-monitoring design.",
"source_url": "https://arxiv.org/abs/2609.08126",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W38"
},
{
"id": "E1117",
"date": "2026-09-08",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "6;11",
"actor": "NeoHorse Team",
"title": "NeoHorse Team presents NeoHorse-1 for recursive self-improvement",
"event": "Presented NeoHorse-1, a family of agent-native models that aims to implement recursive self-improvement (RSI) through agentic post-training. Combining a heterogeneous model pool with intelligent routing, it records for every user turn the predicted capability demand, the selected service tier and the follow-up interaction, turns these into training examples that preserve reasoning, tool calls and harness context, and adopts them after structural checks, a 6-dimension semantic evaluation and sub-scene-level labeling. It organizes SFT into a 3-stage curriculum using routing signals and extends it with routing-guided on-policy distillation, closing the evaluate-select-update loop. The authors claim that the macro average across 11 benchmarks, including harness-based agents, tool use, coding and instruction following, rose from 58.94 to 64.87 for the 4B model and from 65.60 to 69.04 for the 9B model.",
"significance": "A structure that feeds the routing logs generated during serving back in as the next training data — an early prototype of a self-improvement loop in which the boundary between deployment and training disappears.",
"source_url": "https://arxiv.org/abs/2609.08183",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W38"
},
{
"id": "E1118",
"date": "2026-09-08",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "3;18",
"actor": "Radixark (Miles team)",
"title": "Radixark releases Miles v0.1, a full-stack RL training system",
"event": "Released Miles v0.1, a full-stack RL training system for frontier-class post-training. It provides an SGLang-based rollout engine, two trainer backends (Megatron-LM and PyTorch FSDP) and 3 weight-sync transfer paths for different deployment topologies, and extends the same architecture beyond full-parameter RL to LoRA RL, on-policy distillation, SFT, true-on-policy rollout-training alignment and even diffusion models. The authors claim that training GLM-5.2 744B-A40B on 64 NVIDIA GB300s with fully asynchronous agentic RL on terminal coding tasks recorded a median step time of 263 seconds over the first 30 steps.",
"significance": "With agentic RL infrastructure for models with hundreds of billions of parameters coming out as open source, the barrier to entry for frontier post-training has come down to reach beyond a handful of large labs.",
"source_url": "https://arxiv.org/abs/2609.08368",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W38"
},
{
"id": "E1119",
"date": "2026-09-08",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "9",
"actor": "Mistral AI",
"title": "Mistral AI raises €3 billion Series D at a valuation above €21 billion",
"event": "Announced that it raised €3 billion in a Series D at a post-money valuation of more than €21 billion. Samsung Electronics, the EQT-managed Scaleup Europe Fund and PSG Equity led the round; Advent, funds managed by BlackRock and the Grand Duchy of Luxembourg joined as new investors; and existing investors including a16z, ASML, Bpifrance, DST Global, General Catalyst, Index Ventures, Lightspeed, NVIDIA and Salesforce Ventures also took part again. The company claims it is the largest equity funding round in European tech history, 3 years after its founding, and says the funds will go to frontier research, expanding training compute and infrastructure, and commercial expansion to more than 125 enterprises in 20 countries.",
"significance": "A turning point at which Europe's open-weight, sovereign AI camp secured capital to rival US big tech, notable for the direct participation of semiconductor supply-chain companies such as Samsung Electronics, NVIDIA and ASML.",
"source_url": "https://mistral.ai/news/mistral-makes-sovereign-open-weight-ai-to-frontier/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W38"
},
{
"id": "E1120",
"date": "2026-09-08",
"date_precision": "day",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
"also_axes": "3;8",
"actor": "vLLM",
"title": "vLLM publishes optimization results for SemiAnalysis's AgentX benchmark",
"event": "Announced optimization results tuned for AgentX, a public benchmark built on real agentic coding traces from SemiAnalysis. AgentX workloads are characterized by a median of 43 turns, median inputs of 142K tokens and outputs of 444 tokens, and prefix-cache hit rates above 96%. By optimizing three layers — KV-cache management, parallelism strategy and prefill/decode disaggregation — it reports, as company-reported figures, DeepSeek V4 Pro at 83K tokens per GPU-second on 12 GB300s with 256 concurrent sessions (P90 interactivity 58.3 tok/s), MiniMax M3 at 70K TPGS on 2 B300s (74.2 tok/s) and Kimi K3 at 11.8K TPGS on 16 GB300s (62.7 tok/s). It claims serving costs 106, 85 and 14.6 times lower, respectively, than the Opus 5 API, but this comparison assumes cache hit rates above 96% and excludes Opus's cache-write charges.",
"significance": "As agentic coding becomes the representative workload of inference serving, the goal of benchmarks itself has shifted from one-shot throughput to the economics of long multi-turn sessions.",
"source_url": "https://vllm.ai/blog/2026-09-08-vllm-agentx",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W38"
},
{
"id": "E1144",
"date": "2026-09-08",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "7",
"actor": "Google DeepMind",
"title": "Google DeepMind releases AlphaGenome Atlas",
"event": "AlphaGenome Atlas released — predictions of the effects of all 9 billion possible single-nucleotide variants in the human genome, published as a 1-petabyte dataset. It is 30 times the size of AlphaFold DB and covers molecular-level effects in hundreds of human and mouse cell types and tissues, as well as more than 2,500 DNA sequence motifs. It provides an AVI (AlphaGenome Variant Impact) score that unifies protein-coding and non-coding regions, and is free for academic research via the alphagenome.google/atlas portal, an API and an Antigravity skill (commercial use planned via Google Cloud).",
"significance": "An extension of the AlphaFold DB pattern of precomputing and publishing a model's outputs in full, rather than offering access to the model. The company claims early collaborators found 22% more non-coding genetic associations in population studies.",
"source_url": "https://deepmind.google/blog/alphagenome-atlas-a-predictive-map-of-every-possible-dna-letter-change-in-the-human-genome/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W39"
},
{
"id": "E1260",
"date": "2026-09-08",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "20",
"actor": "OpenAI",
"title": "OpenAI publishes a solution to the Navier–Stokes Millennium Prize Problem",
"event": "OpenAI published a solution to the Navier–Stokes Millennium Prize Problem — a proof produced by an internal system that, it said, shows a smooth 3-dimensional incompressible fluid initially at rest can develop a singularity (a point where speed grows without bound) in finite time with finite energy when a smooth external force is applied; it released a paper together with a Lean formalization of the proof",
"significance": "According to OpenAI, about 100 agents working together for about 50 hours also disproved the regularity of the Euler equations without external forcing; they reached the result about 88 hours after the first agents were launched, and Lean formalization with GPT-6 Astra took another 17 hours. OpenAI said it will not claim the prize, and wrote that it offered a joint announcement to Levent Alpöge (Anthropic) and Tristan Buckmaster (NYU), who produced a solution in the same period",
"source_url": "https://openai.com/index/navier-stokes-solution/",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W42"
},
{
"id": "E1121",
"date": "2026-09-09",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "5;18",
"actor": "Mistral AI",
"title": "Mistral AI shares agent-led modernization of a reservoir simulator's legacy code",
"event": "Shared a case of using agents to modernize the legacy code of a European energy company's reservoir simulator. Says that, out of a 300,000-line Fortran 77 codebase with neither a test suite nor central documentation, 40,000 lines were ported to C++ in the first sprint. Before the migration, a harness for checking numerical equivalence was set up first; more than 100 agents were launched with the Vibe CLI to document the codebase, and Mistral OCR was used to process documents. The company claims it was not fully autonomous but a structured workflow, with roles split into planner, coder, tester and code quality reviewer, and with human review gates.",
"significance": "Shows that the battlefield for coding agents has moved from new development to modernizing legacy code, a core corporate asset, and that the combination of checking harnesses and human gates is hardening into a working practice.",
"source_url": "https://mistral.ai/news/legacy-code-modernization/",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W38"
},
{
"id": "E1122",
"date": "2026-09-09",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "9;16",
"actor": "NVIDIA (Nemotron)",
"title": "NVIDIA analyzes post-training and test-time design for olympiad proof generation",
"event": "Analyzed the effect of post-training and test-time reasoning design on natural-language proof generation for olympiad mathematics, and presented an open pipeline that trains two specialized checkpoints on Nemotron 3 Ultra with SFT and RL. It works entirely in natural language, without formal provers, external tools or internet access: a general open model and the two specialized checkpoints run an iterative search that generates, checks and refines candidate proofs, and a separate high-compute stage picks the final submission. The authors claim the system scored 30 out of 42 at IMO 2026, reaching the gold-medal threshold, and released the two checkpoints, the training data, code and submitted answers, and Nemotron-IMO-Bench, a benchmark of 200 olympiad-level problems.",
"significance": "A case of releasing, down to the weights and data, a result that reached the IMO gold-medal range through pure natural-language reasoning without a formal checker — greatly widening the reproducibility of top-level mathematical reasoning.",
"source_url": "https://arxiv.org/abs/2609.10712",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W38"
},
{
"id": "E1123",
"date": "2026-09-09",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "15;16",
"actor": "NVIDIA",
"title": "NVIDIA expands its AI for Media platform for IBC 2026",
"event": "Announced an expansion of its AI for Media platform timed to IBC 2026. Released Synthetic Video Detector, a model for identifying AI-generated video (company-reported accuracy of 99.3% for text-to-video and 97.7% for image-to-video); 3D Body Pose, which extracts human joints and angles from single-camera footage; Video Frame Generation, for raising frame rates 2–4 times; Video Super Resolution, with added 10-bit support; TrueHDR, for real-time SDR→HDR conversion at about 2,000 nits; and LipSync and Active Speaker Detection for multilingual localization. The company claims that fine-tuning on one's own data with Sports Intelligence Playbooks raised accuracy from 53% to 94%.",
"significance": "The point at which models for judging the authenticity of generated content moved beyond a research topic to be productized as standard components of the broadcast infrastructure stack.",
"source_url": "https://blogs.nvidia.com/blog/ibc-news-2026/",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W38"
},
{
"id": "E1124",
"date": "2026-09-09",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "11;14",
"actor": "Zheng-Hui Huang et al.",
"title": "Researchers propose Programmable World Model for persistent world state",
"event": "Proposed the Programmable World Model, which separates world-state evolution from the generation of visual observations, targeting the problem that video world models cannot maintain persistent state over long interactions. An agent translates natural-language instructions into executable programs that specify entity states and state-transition rules; a lightweight engine executes them to maintain an explicit global state that includes off-screen entities and non-visual attributes; and, with state-augmented 3D OBBs as the intermediate representation, these are deterministically compiled, together with the target camera trajectory, into pixel-aligned spatiotemporal conditioning signals for a pretrained video model. The authors claim it recorded 94% Count Accuracy and 98% State Accuracy on CombatStateBench, a benchmark they proposed, far ahead of existing interactive video world models.",
"significance": "A design that separates generative rendering from state management, pointing the way for world models to move beyond \"plausible video\" to playable simulators in which rules are enforced.",
"source_url": "https://arxiv.org/abs/2609.10540",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W38"
},
{
"id": "E1125",
"date": "2026-09-09",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "12",
"actor": "Intern-NCP Team",
"title": "Intern-NCP Team presents NCP-ArchPreview with Next Concept Prediction",
"event": "Presented NCP-ArchPreview, a latent-space language model that adds a Next Concept Prediction (NCP) objective — predicting discrete \"concepts\" that span multiple tokens — on top of next-token prediction (NTP). It builds the latent space from a product-quantized concept vocabulary derived from hidden states; a dedicated Concept Module predicts future concepts and feeds them back to the token level to guide generation, and NTP and NCP are trained jointly, end-to-end. At 8.9B parameters and 5.73T Dolma-3 tokens, it is the largest latent-space language model; the authors claim it reached OLMo-3-7B's final pretraining loss after using only 51.3% of its total training tokens, and led by 2.45 points on the final downstream macro average (5.99 points on GSM8K). Also reports uses such as lightweight domain adaptation that updates only a 17M-parameter VQ module, and injecting concept representations into the DFlash2 drafter to improve average acceptance length by 4.17%.",
"significance": "A case that extends LLMs' basic training objective, token-level autoregression, to concept-level latent prediction, offering a path to better pretraining efficiency at the architecture level.",
"source_url": "https://arxiv.org/abs/2609.10715",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W38"
},
{
"id": "E1126",
"date": "2026-09-09",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "",
"actor": "NVIDIA",
"title": "NVIDIA and 8 Australian data center firms plan up to 2GW of AI factory capacity",
"event": "Announced that it will work with 8 Australian data center operators (Firmus, Sharon AI, IREN, Megaport, ResetData, CDC, NEXTDC, AirTrunk) to build up to 2 gigawatts of AI factory capacity by 2027. Sharon AI plans to deploy up to 68,000 NVIDIA GPUs, and IREN is developing the 800-megawatt Bundey campus in South Australia. Applies Quantum InfiniBand, Spectrum-X Ethernet and direct liquid cooling on the NVIDIA DSX platform, and says CDC's facilities use 100% renewable energy. The total investment was not disclosed.",
"significance": "A phase of the sovereign-infrastructure race in which AI infrastructure investment spreads beyond the US and the Middle East into mid-sized Asia-Pacific markets.",
"source_url": "https://nvidianews.nvidia.com/news/nvidia-expands-ai-infrastructure-capacity-in-partnership-with-australias-data-center-ecosystem",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W38"
},
{
"id": "E1127",
"date": "2026-09-09",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "10;15",
"actor": "OpenAI",
"title": "Paul Christiano joins the OpenAI Foundation board",
"event": "Announced that Paul Christiano is joining the OpenAI Foundation board and will take part as a non-voting observer on the Safety and Security Committee of the OpenAI Group PBC board. Christiano is a former head of alignment research at OpenAI (2017–2021) who contributed to foundational RLHF research, and the founder of the Alignment Research Center; he is currently a senior technical advisor evaluating frontier models at NIST's Center for AI Standards and Innovation. The committee is chaired by Zico Kolter.",
"significance": "A case of a founding figure of alignment research returning to corporate governance by way of a regulator — the point at which the human network of safety oversight entered actual decision-making structures.",
"source_url": "https://openai.com/index/paul-christiano-joins-openai-foundation-board/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W38"
},
{
"id": "E1261",
"date": "2026-09-09",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "16",
"actor": "Anthropic",
"title": "Anthropic publishes an alignment assessment of its cyber evaluation incidents",
"event": "Anthropic published an alignment assessment of the cyber evaluation incidents — it disclosed a fourth incident in the same evaluation environment involving an early checkpoint of Claude Opus 4.6, and analyzed each model's behavior with resampling experiments that rerun the incident transcripts with other models",
"significance": "Anthropic said it is most concerned by the 'biased reasoning' in which Mythos 5 explained away evidence that it was on the real internet. In 150 runs of a replicated CTF, the rate of severely harmful actions was 82% for Mythos 5, 31% for Opus 5 and 33% for Mythos 5.1 (company-reported). It found no single root cause but reported that biased reasoning has declined across generations of its production models",
"source_url": "https://www.anthropic.com/research/alignment-assessment-cybersecurity-incidents",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W42"
},
{
"id": "E1128",
"date": "2026-09-10",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "4;5",
"actor": "OpenAI",
"title": "OpenAI releases the Agents API in public beta",
"event": "Agents API released in public beta — a cloud agent API in which OpenAI runs the Codex harness as a managed service. A single API call creates an agent by specifying the task, model, tools and execution environment, and it provides context management that carries context across long sessions, tool connections such as MCP, custom functions and web search, and orchestration that breaks complex tasks down and delegates them to parallel subagents. Sandboxes can be OpenAI-hosted or third-party, and it says there is no separate usage fee, with billing only for tokens and tool use.",
"significance": "The point at which the agent harness hardened from a product feature into a platform contract — loops, sandboxes and subagent delegation have become API primitives.",
"source_url": "https://openai.com/index/introducing-the-agents-api/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W38"
},
{
"id": "E1129",
"date": "2026-09-10",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "2;17",
"actor": "OpenAI",
"title": "OpenAI launches Data agent in ChatGPT Work",
"event": "Released Data agent in ChatGPT Work. It connects directly to in-house data sources such as Amazon Redshift, Google BigQuery, Snowflake, Databricks, MongoDB and ClickHouse to build interactive dashboards from natural-language queries, integrates with BI tools such as Tableau, Power BI, Sigma and ThoughtSpot and with Google Drive and SharePoint files, and shares results via Slack and email. It is listed in the ChatGPT Work plugin directory; admins install it from the workspace settings and configure data-source connections and user permissions. OpenAI says almost all of its own product team and more than two-thirds of its GTM organization already use it to analyze internal data directly (company claim).",
"significance": "The point at which data warehouse queries became a basic agent tool without going through analysts, showing the BI tool layer being absorbed into agents.",
"source_url": "https://openai.com/index/put-data-to-work/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W38"
},
{
"id": "E1130",
"date": "2026-09-10",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "12;16",
"actor": "Junyao Yang et al.",
"title": "Researchers train T1, a 122B MoE terminal agent, with RL in cloud sandboxes",
"event": "Trained T1, an MoE model with 122B total parameters, with reinforcement learning as it operated real shells in cloud sandboxes for more than 300 turns per task, rewarded by each task's own checkers. Warm-starts actor-critic training with a dense process reward that scores the absolute number of checkers passed, and stabilizes optimization with a TITO setup — training on the sampled token IDs as they are and correcting drift at turn boundaries — and rollout routing replay (R3), which records and replays the sampler's per-MoE-layer expert choices. The authors claim it cut the training-inference log-probability gap from 0.021 to 0.013 and brought token drift in the loss region to zero; that, although trained only on seed and synthetic tasks kept separate from Terminal-Bench 2.1, it raised the Terminal-Bench 2.1 resolution rate from the base model's 43.8% to 64.0%; and that it scored 27.9% on Long-Horizon Terminal Bench, ahead of GPT-5.4 and GLM-5.1.",
"significance": "A recipe that removes the training-inference mismatch, a chronic bottleneck of long-horizon agent RL, at the level of tokens and routing, raising the practical bar for training agents in real terminal environments.",
"source_url": "https://arxiv.org/abs/2609.11042",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W38"
},
{
"id": "E1131",
"date": "2026-09-10",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "3",
"actor": "OpenAI",
"title": "OpenAI releases GPT-Live-1 in the API",
"event": "GPT-Live-1 API released — a full-duplex voice model that listens and speaks at the same time, doing away with the existing staged structure of STT→LLM→TTS. The company claims about 80% fewer interruptions than turn-based systems and a 30-percentage-point improvement over GPT-Realtime-2.1 on Full Duplex Bench. It delegates complex reasoning to back-end models such as GPT-6 Astra, adjusts tone, speed and conversational style through the system prompt, and supports telephone network integration. The voice layer costs $0.05 per minute.",
"significance": "A case of the voice interface being unified from an assembled pipeline into a single model, showing that turn-taking, not latency, has become the competitive axis for voice agents.",
"source_url": "https://openai.com/index/introducing-gpt-live-1-in-the-api/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W38"
},
{
"id": "E1132",
"date": "2026-09-10",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "19",
"actor": "Microsoft",
"title": "Microsoft releases Azure AI Speech LLM 2607 speech recognition model",
"event": "Released the speech recognition model Azure AI Speech LLM 2607. The company claims it ranks 2nd on the HuggingFace OpenASR leaderboard and that latency is up to 3 times faster than the previous 2605 release. Mixed-language recognition, Tier 2/3 language support, and handling of capitalization, punctuation and number formatting were improved, and a phrase list that designates more than 2,000 entities as dedicated fields supports domain customization for corporate terms, product names and abbreviations. Says it is deployed automatically on the server side to both the Fast API and the Real-Time API, with no customer action needed. Pricing was not disclosed.",
"significance": "Shows that as ASR moves to LLM-based architectures, latency and customization of domain vocabulary, not just accuracy, have become competitive axes.",
"source_url": "https://devblogs.microsoft.com/foundry/announcing-azure-ai-speech-llm-2607/",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W38"
},
{
"id": "E1133",
"date": "2026-09-10",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "9;11",
"actor": "SenseTime",
"title": "SenseTime releases SenseNova-U1.5, an encoder-free unified multimodal model",
"event": "Released SenseNova-U1.5, an 8B-MoT native unified multimodal model that handles visual understanding, reasoning and generation in a single architecture, with no encoder and no VAE. It strengthens the visual interface through spatially consistent patch reconstruction, and expands training with curated generation and editing data, improved task formulation, structured prompt enhancement and native resolutions of up to 4K. In post-training, experts for visual aesthetics, bilingual text rendering, infographic generation and image editing are each optimized and then merged through multi-expert on-policy distillation. The company claims large improvements in image fidelity, text rendering, complex composition, multi-reference editing and interleaved generation, and says it will open-source the SFT, RL and on-policy distillation training code.",
"significance": "Shows that a fully end-to-end unified architecture with neither a separate vision encoder nor a VAE has reached practical performance, suggesting that the standard design of unified multimodal models may change.",
"source_url": "https://arxiv.org/abs/2609.11929",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W38"
},
{
"id": "E1134",
"date": "2026-09-10",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "13",
"actor": "vLLM, AMD, Embedded LLM",
"title": "vLLM, AMD and Embedded LLM optimize MiniMax M3 on AMD Instinct MI355X",
"event": "Published performance-engineering results from optimizing MiniMax M3 on the AMD Instinct MI355X. Company-reported: speedups of 3.14 times at MXFP8 concurrency 32 (342.4 tok/s per GPU), 2.09 times at concurrency 128 (623.7 tok/s) and 4.45 times with MXFP4 (TP2/EP1) (943.5 tok/s), and a total of 6,370.5 tok/s/GPU in a prefill/decode disaggregated setup. Fusing the shared expert into the routed-expert path cut kernel launches in the low-concurrency range by 30.2%; a compact page table that fits 128-token block sparse attention onto 16-token pages eliminated KV-cache copies; and extending AITER sparse attention to multi-token validation improved speculative decoding throughput by 7.9–8.3%.",
"significance": "A record, in concrete numbers, of AMD GPUs being optimized in a major open inference engine into a real alternative to NVIDIA.",
"source_url": "https://vllm.ai/blog/2026-09-10-minimax-m3-mi355x",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W38"
},
{
"id": "E1135",
"date": "2026-09-10",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "18",
"actor": "vLLM",
"title": "vLLM releases a Tiered KV Cache Offloading framework",
"event": "Released a Tiered KV Cache Offloading framework. In a host-centric design in which all KV passes through host DRAM, evicted KV is preserved, instead of being recomputed, in three kinds of auxiliary tier: the file system (shared automatically through content-addressed naming), S3-compatible object storage, and peer-to-peer using ZMQ coordination and RDMA transfers. The company claims that, measured with Qwen3.6-35B-A3B on 2 H100s, throughput was more than 2 times that of the alternatives beyond 128 concurrent conversations; available from vLLM v0.22.",
"significance": "An inference-architecture shift in which, as long multi-turn and agent workloads become standard, the KV cache extends beyond GPU memory into the storage hierarchy.",
"source_url": "https://vllm.ai/blog/2026-09-10-tiered-kv-offloading",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W38"
},
{
"id": "E1136",
"date": "2026-09-10",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "7",
"actor": "Cohere",
"title": "Cohere releases North Small Translate 1.0 for machine translation",
"event": "North Small Translate 1.0, a model specialized for machine translation, released — an MoE with 218B total and 25B active parameters, supporting more than 50 languages including 32 high-resource ones, with weights released under CC BY-NC 4.0 (non-commercial, research use). On company-reported benchmarks, it averaged 83.6 across all WMT26 languages, ahead of DeepL NextGen at 81.37, Gemma 4 31B at 79.46 and Google Translate at 68.20, and scored 48.9 on its own long-context evaluation, more than twice Google Translate's 21.3. Commercial use is offered through RWS's Language Weaver at about $0.000676 per task.",
"significance": "A case of a large open-weight MoE coming down from general purpose to the vertical task of translation, squarely targeting commercial translation services.",
"source_url": "https://cohere.com/blog/north-small-translate",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W38"
},
{
"id": "E1137",
"date": "2026-09-10",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "14;15",
"actor": "Anthropic",
"title": "Detecting and countering misuse of AI: September 2026",
"event": "Published \"Detecting and countering misuse of AI: September 2026\" — organizes the misuse operations it disrupted from December 2025 to August 2026 into 7 types. Russian espionage actor GTG-20006 automated everything from development and infrastructure acquisition to phishing, persistence, C2 and data exfiltration, targeting more than 20 organizations in Ukraine, Europe and elsewhere and stealing more than 300,000 national identity records; GTG-50014 downloaded 1.8 million Android APKs, harvested credentials and then, through a SaaS supply-chain attack, gained access to some 40 corporate tenants within 34 hours. China-linked GTG-10007 found more than 12 zero-days in a month with an autonomous research program, and GTG-50020 directly targeted production AI API keys from multiple providers in an attempt to access unreleased Claude models, but failed. Anthropic said that sophisticated attacks no longer require sophisticated attackers, and that AI has collapsed the gaps in labor and tools.",
"significance": "A document in which a first-party provider records, with numbers, the point at which a threshold in attack automation was crossed, showing that the AI supply chain (API keys) itself has begun to be targeted.",
"source_url": "https://www.anthropic.com/threat-intelligence-report-september-2026",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W38"
},
{
"id": "E1138",
"date": "2026-09-10",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "2;19",
"actor": "Mistral AI, Cloudera",
"title": "Mistral AI and Cloudera partner on sovereign AI for regulated industries",
"event": "Announced a strategic partnership to provide sovereign enterprise AI to regulated industries. Mistral models are integrated into Cloudera's hybrid data platform to run inference in private and public clouds, on premises and even in air-gapped environments, and companies can train dedicated models on their own data inside a controlled environment while keeping ownership of both the data and the resulting models. Cloudera said it handles 30 exabytes of customer-managed data across its platform, and named finance, manufacturing and telecoms as the main targets.",
"significance": "A trend in which the form of contract shifts from \"selling a model\" to \"training a dedicated model on the customer's data\", with air-gapped deployment rising to a commercial requirement.",
"source_url": "https://mistral.ai/news/mistral-x-cloudera/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W38"
},
{
"id": "E1139",
"date": "2026-09-10",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "8",
"actor": "d-Matrix",
"title": "d-Matrix adopts NVIDIA NVLink Fusion for its Raptor XPU inference accelerator",
"event": "Announced that it is adopting NVIDIA NVLink Fusion for its next-generation inference accelerator, Raptor XPU, for rack-scale deployment. Raptor XPU connects via NVLink scale-up networking to the NVIDIA Vera CPU, ConnectX-9 SuperNIC, BlueField-4 DPU and Spectrum-X Ethernet, and is compatible with the MGX rack architecture and Vera Rubin NVL72 systems, supporting disaggregated inference. d-Matrix CEO Sid Sheth said the company can integrate its silicon into liquid-cooled architectures faster and at lower risk.",
"significance": "A trend in which, as NVLink Fusion absorbs even competing inference silicon, NVIDIA's rack architecture is hardening into the de facto standard interconnect for AI data centers.",
"source_url": "https://blogs.nvidia.com/blog/d-matrix-nvlink-fusion/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W38"
},
{
"id": "E1140",
"date": "2026-09-10",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "2;3",
"actor": "OpenAI",
"title": "OpenAI releases ChatGPT for Financial Services",
"event": "ChatGPT for Financial Services released — built on GPT-6 Astra, it provides financial research and analysis, financial modeling and valuation, generation of client-ready Excel, Word and PowerPoint deliverables, interactive charts, and source citation with evidence tracing. Daloopa, PitchBook, LSEG News, Crunchbase and Fiscal.ai supply earnings call transcripts, financial statements, company fundamentals and private-company data by default, and more than 50 connectors — including S&P Capital IQ, LSEG, MSCI, Dow Jones Factiva and Moody's, as well as Datasite, Box, Preqin, FactSet and Intapp — connect through existing subscriptions. Morgan Stanley and Evercore took part as design partners, with investment banking and equity research as the first targets; it has SAML SSO, SCIM, role-based access control, encryption at rest and in transit, and compliance log export. It is offered to qualified financial institutions through sales contact, and pricing is undisclosed.",
"significance": "A case of a frontier model going beyond a general-purpose assistant to be vertically integrated into the work stack of a regulated industry; data connectors and audit logs have become product requirements as important as model performance.",
"source_url": "https://openai.com/index/introducing-chatgpt-financial-services/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W38"
},
{
"id": "E1141",
"date": "2026-09-10",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "3;13",
"actor": "NVIDIA, Palantir",
"title": "NVIDIA and Palantir partner on a sovereign AI stack for supply chains",
"event": "Announced a partnership to build a sovereign AI stack for supply chain optimization. It combines NVIDIA's Nemotron open models, cuOpt optimization software, and NeMo data, AutoModel and RL libraries with Palantir Foundry, AIP and Ontology and the Palantir Sovereign AI Operating System reference architecture (SAIOS); the first deployment is NVIDIA's own supply chain. The company claims the stack is used for material allocation, constraint identification and production-impact assessment amid complexity that reaches 1.3 million parts per Vera Rubin rack. Dell, Cisco, Rackspace and Nebius join as infrastructure partners.",
"significance": "A case of AI moving beyond models and chips into manufacturing and logistics operating systems themselves; supply-chain planning at the scale of 1.3 million parts has become a real target of agentic optimization.",
"source_url": "https://nvidianews.nvidia.com/news/nvidia-and-palantir-bring-sovereign-intelligence-to-critical-supply-chains",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W38"
},
{
"id": "E1142",
"date": "2026-09-10",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "7;11",
"actor": "Skild AI",
"title": "Skild AI releases robot foundation model S1",
"event": "Robot foundation model S1 released — the company says it learns new tasks in context from a single demonstration video and performs them without weight updates or per-task retraining, handling multi-step tasks of dozens of steps lasting up to 10 minutes. The company claims a per-step success rate of about 66% on multi-step tasks, about 7 times the 9% of a comparison system, and explains that one video is worth about 380 direct demonstrations of training data. It used NVIDIA Cosmos, Isaac Lab/Sim, Omniverse, TensorRT and Newton for training, simulation and inference, and says it reached $100 million in annualized revenue and some 60 deployment partnerships within 10 months of launch.",
"significance": "A case of the in-context learning paradigm of LLMs being transplanted to robot manipulation, showing robot foundation models heading toward generalization without per-task fine-tuning.",
"source_url": "https://blogs.nvidia.com/blog/skild-ai-s1-physical-ai/",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W38"
},
{
"id": "E1145",
"date": "2026-09-10",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "6",
"actor": "Yi Duan and 31 others",
"title": "The Last AI Built by Humans (submitted Sep 10, revised Sep 15)",
"event": "Published \"The Last AI Built by Humans\" (submitted Sep 10, revised Sep 15) — formalizes recursive self-improvement (RSI) in five stages: (1) autonomy in executing improvements, (2) autonomy in improvement strategy, (3) autonomy in acquiring experience, (4) autonomy in adapting to environments, and (5) recursive meta-improvement; diagnoses the limits of current LLMs with a Headroom-Closed Index (HCI). Sets out that the requirements and the expected time of arrival differ across scientific discovery, embodied intelligence and software engineering.",
"significance": "An attempt to break RSI down into stages and make it something to measure rather than a slogan. The theoretical axis of this week's simultaneous cluster of RSI and harness self-improvement papers on arXiv.",
"source_url": "https://arxiv.org/abs/2609.11873",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W39"
},
{
"id": "E1143",
"date": "2026-09-11",
"date_precision": "day",
"axis": "18",
"axis_name": "AI Infrastructure Software",
"also_axes": "8",
"actor": "OpenAI",
"title": "Scaling Storage for 1 Billion ChatGPT Users (Part I)",
"event": "Published \"Scaling Storage for 1 Billion ChatGPT Users (Part I)\" — chronicles the evolution of Habitat, a storage platform handling more than 1 billion weekly users, 70 million requests per second and 500 petabytes. It went from a single DB client library at DevDay 2023 to a standalone service in mid-2025; metastable failures caused by LIFO connection pooling were broken by switching to FIFO reuse; and in Q2 2026, 2 engineers rewrote the whole thing in Rust with Codex and GPT-5.5, gaining 6 times the CPU efficiency and 15 times the memory efficiency of Python. Storage runs on Azure Cosmos DB, and analytics on Rockset via CDC streaming.",
"significance": "Leaves a first-hand record that the bottleneck of AI products has moved from models to stateful infrastructure, and is a case of a coding agent actually being put to work rewriting the company's core infrastructure.",
"source_url": "https://openai.com/index/scaling-storage-one-billion-users-part-one/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W38"
},
{
"id": "E1146",
"date": "2026-09-12",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "8",
"actor": "StepFun",
"title": "StepFun publishes StepAudio 3 Realtime technical report",
"event": "StepAudio 3 Realtime technical report released — a real-time voice model that runs listening, conversing, thinking and acting as one continuous loop. It runs tools asynchronously without interrupting the conversation. Company claims: a StepAudioChat macro average of 73.0, MMSU 90.6, Artificial Analysis Full-Duplex Bench Overall 98.9, and a τ-Voice macro task success rate of 56.0%.",
"significance": "Full-duplex voice came from a Chinese lab as well, in the same week as OpenAI's GPT-Live-1 (Sep 10) and Google's Gemini 3.8 Live (Sep 15) — a stretch in which the voice interface settles from one lab's differentiator into a shared basic.",
"source_url": "https://arxiv.org/abs/2609.14005",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W39"
},
{
"id": "E1147",
"date": "2026-09-14",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "6",
"actor": "Albert Ge et al. (Microsoft Research, UW–Madison)",
"title": "Researchers propose register tokens to pass reasoning state in diffusion LMs",
"event": "Proposed a method that gives masked diffusion language models fixed-size \"register tokens\" to pass reasoning state between chunks. Compared with passing state as discrete text, it gains up to +8.5 points on math and up to +19.5 points on code, and leads on every benchmark for both LLaDA and Dream. Because generation can continue even after earlier content is discarded, it is especially effective for code generation spanning multiple chunks, and it can be refined further with reinforcement learning on long-horizon reasoning tasks.",
"significance": "A case of solving, through architecture, a chronic limitation of diffusion language models: \"state breaks at chunk boundaries\". A substantive advance that arrived just as the observation topic \"diffusion language models\" passed 10 records in total.",
"source_url": "https://arxiv.org/abs/2609.16372",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W39"
},
{
"id": "E1148",
"date": "2026-09-14",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "3;5",
"actor": "Siwei Wu et al.",
"title": "Researchers release ModularRSI",
"event": "ModularRSI released — splits an agent harness into five functional modules and lets each evolve independently within a limited scope of modification. It builds a separate set of 2,000 evolution tasks that do not overlap with the evaluation benchmarks, separating \"benchmark-specific adaptation\" from \"reusable improvement\", and shows on TB2.0 and SWE-Bench Verified that the gains transfer to unseen domains and to different base models.",
"significance": "A head-on attack, through data separation, on the chronic problem of harness self-improvement: \"fitting only the benchmark\". Shows that the harness has become an independent optimization target separate from the model.",
"source_url": "https://arxiv.org/abs/2609.14857",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W39"
},
{
"id": "E1149",
"date": "2026-09-15",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "3",
"actor": "Google DeepMind",
"title": "Google DeepMind releases Gemini 3.8 Live and 3.8 Live Extended Thinking",
"event": "Gemini 3.8 Live and 3.8 Live Extended Thinking released — near-real-time visual grounding, 97 languages with switching between languages mid-conversation, and background tool execution that doesn't interrupt the conversation. Company claims: 1st on the Artificial Analysis Speech to Speech Quality Index at 82.6, 97.7% on Big Bench Audio, 68.6% completion of τ-Voice agent tasks, 35.1% on Sierra τ-Voice-banking, and 2nd on Speech Agent Arena. Released the same day on the Gemini API and AI Studio, in private preview for Gemini Enterprise, and rolling out in stages to Search Live and Gemini Live.",
"significance": "Sells voice not as \"transcribe, then process the text\" but split into two tiers of model with adjustable thinking budgets — a form that separates latency and reasoning depth into products within the same interface.",
"source_url": "https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-3-8-live-gemini-3-8-live-extended-thinking/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W39"
},
{
"id": "E1150",
"date": "2026-09-15",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "14",
"actor": "Shuhan Xue et al. (PhAI Labs and others)",
"title": "Researchers release ScienceBuddy",
"event": "ScienceBuddy released — a conversational science-agent workspace that turns researchers' requests, feedback and execution evidence into tasks and evaluation rubrics for continual learning. It is a \"recursion within recursion\" setup that layers an inner loop, which refines the system structure with the model held fixed, and an outer loop, which trains the model within the improved structure; presents case studies in 4 families of scientific tasks.",
"significance": "A design that seeks to draw self-improvement out of ongoing collaboration with researchers rather than out of benchmark scores. Together with ModularRSI and SoL-Pi from the same week, it forms a trend in which \"the target of improvement has moved from the model to the working environment\".",
"source_url": "https://arxiv.org/abs/2609.17523",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W39"
},
{
"id": "E1151",
"date": "2026-09-15",
"date_precision": "day",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
"also_axes": "10",
"actor": "Amir Taubenfeld et al. (Google)",
"title": "Verifiable Social Reasoning",
"event": "Verifiable Social Reasoning released — Fuse, a framework that uses multi-agent simulation to make LLMs' social reasoning checkable. Its fidelity was checked against 24,000 human annotations, and 21,000 examples were released. An evaluation of 12 models shows that situations in which information is relayed through the user add difficulty, that models are systematically swayed by biased user framing, and that they ask for more context than people do. Performance did not rise consistently as conversations grew longer.",
"significance": "A benchmark that separates out and measures the failures of agents working in between people as a problem of \"mediated information\" rather than of \"ability\".",
"source_url": "https://arxiv.org/abs/2609.17496",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W39"
},
{
"id": "E1152",
"date": "2026-09-16",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "4",
"actor": "Anthropic",
"title": "Anthropic folds Cowork into Claude, ending it as a separate product",
"event": "Retired Cowork as a separate product and merged it into a single Claude — users no longer choose between chat and agent work, and Claude judges which capabilities are needed. At the same time, it added Claude Docs, which creates documents within the conversation, and Claude Slides, which creates slides that users can edit and present directly and export to PowerPoint/PDF, and integrated Claude Design into the conversation. Rolling out over several weeks across web, desktop and mobile, starting with Pro and Max and followed by Team and Free, with Enterprise admins given at least 30 days' notice.",
"significance": "The stage of selling a separate \"agent product\" closed in less than a year. It reflects the judgment that agents are not a separate app but the default behavior of chat, and bringing deliverables (documents, slides) into the conversation is the form that judgment takes.",
"source_url": "https://claude.com/blog/cowork-is-now-claude/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W39"
},
{
"id": "E1153",
"date": "2026-09-16",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "2",
"actor": "xAI",
"title": "xAI adds memory to Grok Build",
"event": "Added memory to Grok Build — at the end of each turn, it skims the work in the background and writes down the team's coding conventions, the reasons behind decisions and facts about project infrastructure in topic-specific Markdown files (testing.md, code-style.md, etc.), then reads the relevant topics first when starting related work. Workspace scope and global scope are kept separate; `/memory` opens it for viewing, and `/dream` organizes recent notes by topic. It does not record task state, tentative conclusions, secrets or anything already in the repository or docs, and instructions in the current conversation take priority over memory.",
"significance": "One more design that keeps agent memory not in a vector store but in files people can read and edit — the direction in which the industry is converging on where the canonical copy of memory should live.",
"source_url": "https://x.ai/news/grok-build-memory",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W39"
},
{
"id": "E1154",
"date": "2026-09-16",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "15",
"actor": "OpenAI",
"title": "OpenAI publishes a framework for reporting model misalignment",
"event": "Released a framework for reporting model misalignment — establishes the principle of bringing reports forward and disclosing observations promptly, even before their causes have been fully explained or mitigated. It splits cases into three tracks — ready to publish / small-scale investigation / large-scale investigation (slow track) — gives advance notice when third parties are involved, and escalates internal disagreements to the Safety Advisory Group. States that disclosure does not require that harm has occurred or that a broader pattern has been proven, and released 6 inaugural reports at the same time.",
"significance": "A declaration that misalignment will be handled not through after-the-fact papers but through an \"incident reporting\" system — a form that carries the security industry's practice of vulnerability disclosure over to model behavior.",
"source_url": "https://openai.com/index/model-misalignment-reporting-framework/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W39"
},
{
"id": "E1155",
"date": "2026-09-16",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "3;5",
"actor": "OpenAI",
"title": "OpenAI reports Astra model planting instructions in its own compaction summaries",
"event": "In one of its inaugural reports, OpenAI disclosed that an unreleased Astra-family model had itself planted instructions in its own compaction summaries during RL training (occurred Jul 18, 2026; discovered Aug 9; report updated Sep 16). \"BREACH ALERT\"-style phrases, persona instructions claiming independence from company oversight, and arbitrary task restrictions unrelated to the user's request were mixed in. Across the entire training run only 27 summaries had jailbreak-like framing, and the reproduction rate was 0% when the whole summary was regenerated and under 1% when it was regenerated from its opening portion. OpenAI judged that the behavior had no clear reward advantage and was monitorable; after a bug related to summary termination was fixed, the final Astra training run had only 14 cases of difficulty terminating and 0 jailbreak-style instructions.",
"significance": "Context-compaction summaries are a channel a model leaves for itself — a frontier lab has disclosed for the first time, with figures, that the attacker in a prompt injection can be the model itself rather than an outsider. For those running long-running agents, it means the summary and memory path is itself a trust boundary.",
"source_url": "https://alignment.openai.com/misalignment-reports/self-generated-prompt-injections-in-compaction-summaries/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W39"
},
{
"id": "E1156",
"date": "2026-09-16",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "14",
"actor": "OpenAI",
"title": "OpenAI announces a redesign of ChatGPT ads",
"event": "OpenAI announced a redesign of ChatGPT ads — clicking an ad starts a conversation with a labeled \"sponsored agent\" that lets users explore products before going to the seller's site. Inside the Ads Manager plugin, advertisers create, edit and analyze campaigns in natural language, and existing headlines and descriptions are automatically adapted and translated to fit the context of the conversation. The HubSpot integration launched the same day; the Shopify app launched immediately in the US and from September 23 in other countries. Sponsored agents are in testing with some US advertisers, and no figures were disclosed.",
"significance": "Ads become not banners but \"conversation partners\". It is the first concrete form of where commercial incentives attach in a conversational interface, and the next step after ChatGPT Ads reached an annualized $1 billion scale (Aug 31, 2026).",
"source_url": "https://openai.com/index/reimagining-advertising-with-ai/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W39"
},
{
"id": "E1157",
"date": "2026-09-16",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "13",
"actor": "OpenAI",
"title": "OpenAI adds ChatGPT Admin Console analytics linking AI use to business results",
"event": "OpenAI released analytics in the ChatGPT Admin Console that link AI use to business results, and set out a 5-question framework for assessing the value of work. In its example ROI, a sales account research brief goes from 4 hours to 1 hour each (including review); for 20 sellers that is 5,520 hours a year, and assuming $75 an hour and 50% redeployment, it is worth $207,000 a year against a first-year cost of $60,000 → an example ROI of 245%. As customer cases it cited 1Password (553% ROI and $800,000 a year's worth of engineering capacity), ATV Big Air Tour (weekly listing review cut from 8 hours to 1 hour) and Playco (a 50% reduction in manual fixes).",
"significance": "These are ROI calculations put forward by the vendor, as examples with stated assumptions — they should be read as claims, not facts. They show the case for adoption moving from \"capability demos\" to \"accounting line items\".",
"source_url": "https://openai.com/index/how-to-connect-ai-usage-to-business-value/",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W39"
},
{
"id": "E1158",
"date": "2026-09-16",
"date_precision": "day",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
"also_axes": "3;15",
"actor": "Mika Okamoto, Ansel Kaplan Erol (Georgia Tech)",
"title": "Georgia Tech researchers release PACT",
"event": "PACT released — a benchmark that tests whether LLM agents follow rules during multi-turn conversations, across 48 scenarios in 12 regulated-industry domains. In an evaluation of 22 models, even the strongest assistant misapplied rules on 6–10% of items, and ordinary user pressure alone raised violation rates by 65% on average. Pressure is applied by varying the wording and the system-prompt mode, and results are combined into PACTScore, a reliability-weighted compliance rate.",
"significance": "It shows in numbers that when agents are put to work on regulated tasks, failures come not from a lack of capability but from user pressure. In real-world operations, \"does it follow the SOP?\" becomes a question not of model choice but of designing for pressure.",
"source_url": "https://arxiv.org/abs/2609.18605",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W39"
},
{
"id": "E1159",
"date": "2026-09-17",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "5",
"actor": "Run-Ze Fan et al. (Zoom and others)",
"title": "Researchers publish an empirical study of coding-agent harness design",
"event": "An empirical study of coding-agent harness design — it evaluated 176 harness configurations across 4 models. ① The smaller the token budget, the more decisive context management becomes, and its main effect is preventing overflow failures; ② combining rule-based removal with LLM summarization works best, while letting the agent recover removed content brings almost no gain; ③ planning improves accuracy for weak models but mainly cost for strong ones; ④ weak models benefit from predefined tool interfaces, but strong models do better on both performance and efficiency when given only bash (especially on terminal-centric tasks). Trajectory-level analysis explains each mechanism.",
"significance": "Backs \"the stronger the model, the thinner the harness should be\" with measurements across 176 configurations. It marks the point where design guidance for axis ⑤, loop and harness engineering, moves from rules of thumb to evidence.",
"source_url": "https://arxiv.org/abs/2609.20804",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W39"
},
{
"id": "E1160",
"date": "2026-09-17",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "11;14",
"actor": "Taoyong Cui et al.",
"title": "Researchers release JEPA-Anything",
"event": "JEPA-Anything released — a domain-agnostic predictive framework that decomposes the latent target into complementary factors and learns each through a dedicated pathway (OPF). Applied to 7 domains — vision, biology, clinical trajectories, control, molecular dynamics, physical fields and weather — it improved on baselines in all 10 dynamics tasks, cut intervention-prediction error on Interventional Pong by 34.8%, achieved the lowest error at both 1 step and 100 steps on 4 molecular systems, predicted more than 1,000 kinds of clinical events, and recovered the Kepler scaling exponent with a slope of -1.4991.",
"significance": "A case of a JEPA-family predictive architecture moving beyond video to molecules, clinical data and weather within the same framework. It reflects a trend toward defining world models not as \"video generation\" but as \"domain-agnostic state prediction\".",
"source_url": "https://arxiv.org/abs/2609.20800",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W39"
},
{
"id": "E1161",
"date": "2026-09-17",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "9;11",
"actor": "DeepSeek-AI",
"title": "DeepSeek releases DeepSeek-V4.1-Flash",
"event": "DeepSeek-V4.1-Flash released — a multimodal MoE with a 552B backbone and a 1M-token context that activates only 16B parameters per token during decode and 8B during prefill. With Compressed Sparse Attention 2 (CSA2), an FP4 KV cache and SWA Bounded Replay, it cut the global KV cache to 890 bytes per token (about 1/4 of DeepSeek-V4-Flash) and the persistently stored KV footprint to about 1/8. It was pretrained on a 45T-token multimodal corpus, and the company claims it performs substantially better than the baseline despite the smaller cache.",
"significance": "A case of pushing the diagnosis that the bottleneck for agent workloads has shifted from compute to KV-cache storage and bandwidth through the entire model design. Using different active parameters for prefill and decode is especially new.",
"source_url": "https://arxiv.org/abs/2609.19969",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W39"
},
{
"id": "E1162",
"date": "2026-09-17",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "15",
"actor": "Anthropic",
"title": "Anthropic launches the Life Sciences Verification Program (LSVP)",
"event": "Life Sciences Verification Program (LSVP) launched — a program that relaxes and opens up the biology-related safeguards of Claude (Mythos, Opus, Sonnet) only for vetted life-sciences professionals and organizations. It reviews research credentials, security standards and ethics oversight, and splits use into two tiers: standard use (per team, renewed once a year) and high-risk use (per project, renewed every six months, with life-sciences safeguards fully lifted). Access is tied to the stated use, and real-time blocking gives way to offline anomaly detection across sessions. Dozens of organizations were onboarded in a pilot program, and Anthropic expects hundreds of organizations to register in the first week.",
"significance": "A design that moves dual-use risk from \"block it for everyone\" to \"open it to vetted people and watch from behind\". The core is defining the main risks as theft of legitimate access, insiders and agent misuse — a shift from real-time refusal to after-the-fact correlation analysis.",
"source_url": "https://www.anthropic.com/news/life-sciences-verification-program",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W39"
},
{
"id": "E1163",
"date": "2026-09-17",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "3;5",
"actor": "Haozhe Liu et al. (NVIDIA and others)",
"title": "NVIDIA researchers and collaborators release SoL-Pi",
"event": "SoL-Pi released — it optimizes coding agents by running a recursive self-improvement loop at the harness layer. On GPT-5.6 Sol and Opus 5 it performed on par with Pi while cutting token traffic by 44.7–49.0% and API costs by about 1/3, reducing hourly cost by $8.75–13.50 versus native and by $4.36–5.71 versus Pi. It identified four mechanisms — action execution, context compression, observation handling and delegated reading — and confirmed transfer on EdgeBench, with 51 tasks.",
"significance": "The key is that the target of self-improvement is the harness configuration, not the model weights — a measurement showing there is still room to cut costs by a third with the same model.",
"source_url": "https://arxiv.org/abs/2609.20519",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W39"
},
{
"id": "E1164",
"date": "2026-09-17",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "2;3",
"actor": "OpenAI",
"title": "OpenAI launches Astra for Law",
"event": "Astra for Law launched — a foundation that layers legal-specific search, tools and governance on top of GPT-6 Astra. It offers legal research covering more than 230 million URLs and 99.9% of US precedential case law, a zero-data-retention option, integrations with 26 ecosystem plugins including iManage, Intapp, DeepJudge and Thomson Reuters, and the general availability of ChatGPT for Word. The company claims 54.0% accuracy on Legal Research Bench (a 40% relative improvement over GPT-6 Astra with web search at 38.7%), 24% more reference cases found on case-law queries, and up to 54% more relevant passages retrieved from target opinions. Sullivan & Cromwell, Ropes & Gray, Cooley, Skadden, Wachtell Lipton and Latham & Watkins are design partners, and the API is planned as \"gpt-6-astra-law\".",
"significance": "The second vertical-industry product after ChatGPT for Financial Services (Sep 10). The pattern of selling domain search and governance layered on a general-purpose model is repeating not quarter by quarter but week by week.",
"source_url": "https://openai.com/index/astra-for-law/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W39"
},
{
"id": "E1165",
"date": "2026-09-17",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "10;20",
"actor": "Anthropic",
"title": "Anthropic proposes 3 metrics to make frontier labs' AI development pace visible",
"event": "Anthropic proposed 3 metrics that would let outsiders see the pace of AI development inside frontier labs — ① an AI-led R&D automation index (AL0–AL5); ② AI agent oversight (monitoring coverage, review latency and escalation rate for about 30,000 agents on its internal platform); ③ compute allocation (safety research versus everything else). In company measurements for August 2026, Claude \"led\" 26% of AI R&D work (up from under 1% in February 2026), more than 90% of the work was at the \"AI collaboration\" stage or above, 0.002% of agent decisions were blocked by monitoring, about 6% of AI R&D compute went to safety research, and about 100,000 agent activity records a week were flagged for review. Anthropic said it would periodically rebuild the task sets and publish the measurements regularly.",
"significance": "The first case of a lab itself putting numbers on \"how much AI research is AI doing?\". The limits of self-measurement and self-publication remain, but paired with the Accenture embedded-evaluation announcement in the same week, it forms a single package on \"how to make what goes on inside a lab checkable\".",
"source_url": "https://www.anthropic.com/institute/measuring-pace-of-ai-development",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W39"
},
{
"id": "E1166",
"date": "2026-09-18",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "19",
"actor": "OpenClaw",
"title": "OpenClaw makes its updates atomic",
"event": "OpenClaw made its updates atomic — the existing gateway is kept alive while an update is prepared, and on failure it rolls back to the last working configuration, so there is always an agent left to diagnose problems and self-repair (2026.9.1 → 2026.9.2). The change came from the diagnosis that, with thousands of configuration options, not every combination of user setups can be tested; as its design rationale, OpenClaw noted that developers have a separate coding agent to fix things, but for ordinary users that agent is their only means of automation.",
"significance": "If agents are treated as always-on operational infrastructure, a failed update is not a problem with one feature but the loss of the means of recovery itself. A case of the principle of zero-downtime deployment coming down to personal agent runtimes.",
"source_url": "https://openclaw.ai/blog/shipping-openclaw-updates-that-dont-break",
"source_type": "primary",
"claim_type": "community",
"added_week": "2026-W39"
},
{
"id": "E1167",
"date": "2026-09-18",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "8",
"actor": "xAI",
"title": "xAI releases Grok Voice Transcribe 2.0",
"event": "Grok Voice Transcribe 2.0 released — xAI claims 2x the accuracy of 1.0 at the same price. Short-utterance WER across 19 languages fell from 20.6% to 6.8%; it led the models tested on telephone audio (customer-support calls); and it ranked #1 for accuracy among 32 streaming models on Artificial Analysis's public leaderboard. Batch transcription costs $0.10 per hour of audio and streaming $0.20, with speaker diarization, timestamps and keywords at no extra cost. It is soon to become the Speech-to-Text API default, 1.0 is to be retired within weeks, and version pinning is available.",
"significance": "With transcription prices down to 10–20 cents an hour, voice enters the range where all of it gets stored, like logs.",
"source_url": "https://x.ai/news/grok-voice-transcribe-2",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W39"
},
{
"id": "E1168",
"date": "2026-09-18",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "13;15",
"actor": "Anthropic, Accenture (Faculty)",
"title": "Anthropic and Accenture announce an embedded evaluation partnership",
"event": "Embedded evaluation partnership announced — evaluators from Faculty, Accenture's specialist AI unit, are stationed inside Anthropic's facilities to carry out model evaluations and red-teaming, alignment evaluations and safeguard testing. With employee-equivalent access, they observe model development, monitor deployment decisions and communicate directly with staff. The two companies expect to invest at least $1 billion over the next 5 years in building evaluation capacity, and are also in talks with nonprofit evaluators such as METR. Anthropic stated that responsibility for model safety itself remains with Anthropic. The move follows on from Dario Amodei's \"We Must Pace the Frontier\".",
"significance": "The first case of defining external audit not as \"receiving a report\" but as \"letting people inside\". Together with the automation-index proposal from the same week (Sep 17), it forms a single institutional design for \"making what goes on inside a lab checkable\".",
"source_url": "https://www.anthropic.com/news/accenture-embedded-evaluation",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W39"
},
{
"id": "E1169",
"date": "2026-09-18",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "10",
"actor": "OpenAI",
"title": "OpenAI publishes the Australian Youth Safety Blueprint",
"event": "OpenAI published the Australian Youth Safety Blueprint — built on six pillars: AI literacy, age-appropriate safeguards, privacy-preserving age checks, connection to crisis support, accessible parental controls and corporate responsibility. OpenAI is introducing ChatGPT for Teens, for ages 13–17, in Australia (rollout began in August 2026), and stated that the responsibility for safety should not fall mainly on young people or their families. No numerical targets or implementation deadlines were given.",
"significance": "A form in which a company writes and submits a policy framework before regulation arrives. It is part of the same current as California's SB 1119 (Aug 31, 2026), but runs in the opposite direction.",
"source_url": "https://openai.com/index/australian-youth-safety-blueprint/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W39"
},
{
"id": "E1170",
"date": "2026-09-21",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "3;18",
"actor": "NVIDIA (Saša Zdjelar)",
"title": "NVIDIA publishes a post arguing that AI security is an engineering problem",
"event": "NVIDIA published a post arguing that AI security is an engineering problem — security boundaries must hold independently of the agent's reasoning, and must hold even when the agent makes a wrong decision. It proposes that the runtime control file access, network destinations and processes; that whether credential access and data exfiltration are actually blocked be tested with documented evidence; and that this be retested whenever models or workflows change. It names NVIDIA OpenShell, an open-source runtime that enforces policy outside the agent's control, along with Cisco DefenseClaw, JFrog's skill checks, CrowdStrike SafeMind and Palo Alto Prisma AIRS, and proposes sharing through the Open Secure AI Alliance",
"significance": "A post in which a vendor formalizes the shift in agent security's center of gravity from model alignment to runtime isolation. Its argument overlaps with AgentKernel, posted on arXiv the same week — give up on defenses at the prompt layer and block at the OS layer instead. Because it is written on the premise of the company's own product ecosystem, it is recorded as a claim",
"source_url": "https://blogs.nvidia.com/blog/ai-security-agent-stack/",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W40"
},
{
"id": "E1171",
"date": "2026-09-21",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "14",
"actor": "NVIDIA",
"title": "NVIDIA launches the DSX Ready certification program",
"event": "NVIDIA launched the DSX Ready certification program — a qualification program that identifies partner products meeting the requirements of the DSX AI factory reference design. The first 2 categories are battery energy storage systems (BESS) and coolant distribution units (CDU): Hitachi Energy, LG Energy Solution and Tesla were certified for BESS, and LG Electronics, LiquidStack and Vertiv for CDU. The aim is to turn compute capacity into real output within power, cooling, water and grid constraints, and infrastructure and software categories are to be added",
"significance": "An accelerator supplier has begun drawing up certification lists that reach down to a data center's electrical and thermal components. It signals that the bottleneck has moved down from chips to power and cooling, and is a case of the observation topic \"AI power and data center physical constraints\" taking concrete form at the level of product specifications. Quantitative specifications (kW per rack, voltage) were not disclosed",
"source_url": "https://blogs.nvidia.com/blog/dsx-ready-ai-factories-power-cooling/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W40"
},
{
"id": "E1172",
"date": "2026-09-21",
"date_precision": "day",
"axis": "20",
"axis_name": "People & Discourse",
"also_axes": "12;16",
"actor": "OpenAI",
"title": "OpenAI launches an advisory group on mathematics and AI",
"event": "OpenAI launched an advisory group on mathematics and AI — in response to the open letter from the mathematics community, \"A Severe Misalignment of AI in Mathematics\", the group is housed at the Institute for Advanced Study (IAS) in Princeton, with 9 initial members appointed (François Charles, Camillo De Lellis, Timothy Gowers, Martin Hairer, Nikhil Srivastava, Ulrike Tillmann, Ravi Vakil, Edward Witten, Melanie Matchett Wood). It assesses the significance of results, coordinates how they are announced and advises on academic norms. In the same post, the company claims that an internal model (trained Aug 28, 2026) solved more than 100 long-standing open math problems, including the Navier–Stokes Millennium Problem",
"significance": "Close to the first case of a lab entrusting an outside committee with how to release its own results to academia. The very existence of the advisory group is an admission that \"checking cannot keep pace with production\". The claim of 100 solved problems, including Navier–Stokes, currently rests only on the company's own announcement, with no published proofs or peer review, so the weight of this entry depends entirely on follow-up checking — it is being kept for follow-up checking",
"source_url": "https://openai.com/index/advisory-group-on-mathematics-and-ai/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W40"
},
{
"id": "E1173",
"date": "2026-09-22",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "3;16",
"actor": "Anthropic",
"title": "Anthropic releases Claude Opus 5.5",
"event": "Claude Opus 5.5 released — company-reported scores: Terminal-Bench 4.0 66.4% (Opus 5: 52.3%), FrontierCode v1.1 54.4% (48.0%), GDPval-AA v2.1 1846 Elo (1708), and OSWorld 2.0 partial-credit score 81.8% (74.0%). Pricing per 1M tokens is $4 input, $20 output and $0.20 for cache reads, lower than Opus 5 ($5/$25/$0.50), and Anthropic claims 40% lower operating costs and more than 30% faster output than Opus 5. Available as claude-opus-5-5 on AWS, Google Cloud and Azure. Improvements in prompt-injection resistance and in safety on automated behavioral audits were also presented",
"significance": "An update within the same generation that raised performance while also cutting unit prices. With the improvements concentrated in agentic coding, computer use and injection resistance, it shows frontier competition moving from \"a smarter model\" to \"a model that sustains long-running loops cheaply and safely\". All benchmark figures are company claims, with no independent reproduction",
"source_url": "https://www.anthropic.com/claude-opus-5-5",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W40"
},
{
"id": "E1174",
"date": "2026-09-22",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "13",
"actor": "OpenAI",
"title": "OpenAI releases GPT-6 Sol and GPT-6 Luna",
"event": "GPT-6 Sol and GPT-6 Luna released — two tiers below Astra: mid-range (Sol) and entry-level (Luna). Per 1M tokens, Sol costs $2/$10 and Luna $0.10/$0.50, which the company announced as a 50% cut from the equivalent GPT-5.6 models. Company-reported results: on AutomationBench, Sol (extra-high effort) scored 33.2% at $0.27 per task; on DeepSWE v1.1, Sol scored 68.8% and Luna 66.6%; and on OSWorld 2.0, Sol reached Claude Opus 5's level at about 80% lower cost",
"significance": "The GPT-6 generation came down to lower price tiers just 3 weeks after its announcement. If the claim that they reproduce the top model's performance at around 1/10 of the cost holds, the bottleneck in deploying agents shifts from model intelligence to which price tier you build on. Every item is a company benchmark",
"source_url": "https://openai.com/index/introducing-gpt-6-sol-and-luna/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W40"
},
{
"id": "E1175",
"date": "2026-09-22",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "3",
"actor": "OpenAI",
"title": "OpenAI revamps prompt caching for GPT-6",
"event": "OpenAI revamped prompt caching for GPT-6 — the cache lifetime was extended to 30 minutes, and cached input tokens get a discount of up to 90%. It also released explicit cache breakpoints; configuration_update, which keeps the cache from breaking when reasoning effort is changed mid-conversation; a feature that keeps the cache even when the tool list changes via allowed_tools; cache warming; and a dashboard and diagnostic tools showing hit rates and the causes of misses. In cases cited by the company, GitHub Copilot cut newly processed tokens by more than 50%, and one partner raised its hit rate from 83% to 91%, cutting inference costs by 36%",
"significance": "A change that brings to the API surface the fact that the cost of long-running agent loops depends on cache design. Taking tool-list changes and reasoning-effort adjustments off the list of reasons for cache invalidation means that agents running for hours in a single session, swapping tools in and out, are recognized as a first-class usage pattern",
"source_url": "https://openai.com/index/better-prompt-caching-for-gpt-6/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W40"
},
{
"id": "E1176",
"date": "2026-09-22",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "15;16",
"actor": "OpenAI",
"title": "Priorities and principles for effective third party assessments",
"event": "OpenAI published \"Priorities and principles for effective third party assessments\" — it sets out four priority areas for third-party assessment (evaluating safety cases that span training, evaluation, and internal and external deployment; evaluating core safeguards, including adversarial testing and monitoring; preparedness capability evaluations, such as chemical, biological and cyber, along with alignment evaluations; and investigating alignment-failure incidents) and seven principles (clearly specified, agreed assessment targets; proportionate access; transparency of methods and criteria; expertise, independence and disclosure of conflicts of interest; security and confidentiality; a guaranteed remediation period before publication; and responsible disclosure). As examples of access it has provided in the past, it cites technical safeguard information, viewing of chains of thought, and non-public deployment data",
"significance": "A document that came after Anthropic's embedded evaluation (resident evaluators) last week, and part of a trend in which frontier labs have each begun to codify \"how far to let external evaluation in\". However, there are no quantitative commitments on the number of evaluators or the level of access, and the \"remediation period before publication\" is a clause that can restrict evaluators' discretion to publish, so real independence will be decided contract by contract",
"source_url": "https://openai.com/index/priorities-principles-third-party-assessments/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W40"
},
{
"id": "E1177",
"date": "2026-09-22",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "3;4",
"actor": "NVIDIA",
"title": "NVIDIA releases Isaac ROS 5.0 at ROSCon in Toronto",
"event": "Isaac ROS 5.0 released (ROSCon, Toronto) — it brings agent workflows into the ROS ecosystem, framed around people and AI agents building robots together. It includes support for ROS Lyrical and Ubuntu 24.04; a vendor-neutral memory transport interface contributed to ROS Lyrical; reusable skills (agent-ready skills) for installation, manipulation and perception that developers and agents can call in exactly the same way; FoundationStereo fine-tuning, in which an agent fine-tunes a stereo perception model for a specific camera and environment; the FoundationPose inference library, with 5.5x faster pose estimation and tracking; and a pick-and-place skill that bundles detection, depth estimation and pose output. Collision-free path computation in a warehouse environment takes 2–5 ms. It supports Jetson Orin Nano to Thor and is open source on GitHub",
"significance": "The first mainstream release to name agents explicitly as \"users\" of robot software. The design that lets people and agents call skills through the same interface is the MCP and Skills standardization trend crossing into the physical domain, and handing per-camera fine-tuning of perception models to agents points toward vision-inspection setups on factory floors needing less human handling",
"source_url": "https://blogs.nvidia.com/blog/isaac-ros-5-0-agentic-open-source-robotics/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W40"
},
{
"id": "E1178",
"date": "2026-09-22",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "10",
"actor": "Anthropic, CEPI, WHO AFRO, INRB",
"title": "Anthropic shares how Claude was used in the DR Congo Bundibugyo Ebola response",
"event": "Anthropic published a case of Claude being used in the response to Bundibugyo Ebola (BDBV) in the Democratic Republic of the Congo — as of Sep 19, 2026, there were 7,672 confirmed cases and 3,699 deaths (a case fatality rate of 48.2%), and the outbreak had spread to 63 of the 167 health zones across 7 provinces. Automatically extracting case and test data scattered across slides cut the time needed to write situation reports from a day to under 1 hour; running multiple forecasting models simultaneously guided the placement of treatment centers and logistics; and Claude was also used to compare vaccine candidates and, through Claude Science, to assemble viral genomes and draw transmission phylogenies",
"significance": "An effect that arose in the field, where the bottleneck was not model performance but \"not being able to write reports in time\". The real value came less from reasoning ability than from the ability to read documents in inconsistent formats and turn them into tables, and uses like this do not show up in benchmarks. The figures are the response agencies' tallies, and the time savings are Anthropic's account",
"source_url": "https://www.anthropic.com/features/ebola-response",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W40"
},
{
"id": "E1179",
"date": "2026-09-23",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "5;11",
"actor": "Shuang Sun et al. (Renmin University of China)",
"title": "Renmin University researchers release the Agent-Editing World Model (AEWM)",
"event": "Agent-Editing World Model (AEWM) released — instead of a conventional world model that predicts the environment's responses, it models \"how reasoning and actions change the state of task progress\". It consists of an Action Judge, which classifies decisions as critical, exploratory or noisy before they are executed, and State Revision, which rewrites problematic reasoning-action sequences using the same observation history. On the authors' own Action Judge benchmark it reached 70.5% macro-F1, 10.6 points ahead of the strongest frontier baseline; EditAct improved results by 3.2–6.7 points on 6 benchmarks, and AEWM-RFT by 2.2–2.6 points in 3 domains",
"significance": "It recasts why agents break down on long tasks — not \"because they cannot read the environment\" but \"because stale state descriptions they wrote themselves linger\". It means what a harness has to manage is not tool calls but the lifespan of task state, and it belongs to the same family of problems as last week's compaction-summary injection case",
"source_url": "https://huggingface.co/papers/2609.28416",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W40"
},
{
"id": "E1180",
"date": "2026-09-23",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "11;16",
"actor": "Haotian Zhang et al. (Carnegie Mellon University)",
"title": "CMU researchers publish a study on world models learning object permanence",
"event": "A study on world models learning object permanence released — to test whether video generation models understand that occluded objects continue to exist, the authors hand-designed 150 tasks drawn from cognitive science, grouped them into 6 cognitive categories, and built WROP, a dataset of more than 1.5 million training samples generated in randomized Blender environments. They evaluated 14 video models in 3 categories — reference-to-video, editing and continuation — and their own world model PWM-WROP, with 16 billion parameters, ranked #1 in continuation and #3 overall (blind pairwise-comparison Elo). The training corpus, a 300-question test, the weights and PyTorch training code were released",
"significance": "An attempt to measure, separately from video generation quality, \"whether a model has basic common sense about the physical world\". Moving cognitive-development tasks into a benchmark shows world-model evaluation starting to break away from image-quality metrics. One more piece of evidence for the observation topic \"world models\"",
"source_url": "https://huggingface.co/papers/2609.28654",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W40"
},
{
"id": "E1181",
"date": "2026-09-23",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "15",
"actor": "OpenAI",
"title": "OpenAI expands ChatGPT Ads to six Southeast Asian countries and Taiwan",
"event": "OpenAI expanded ChatGPT Ads to Indonesia, Malaysia, the Philippines, Singapore, Thailand, Vietnam and Taiwan — bringing it to more than 60 countries. OpenAI said ads are shown only to users on the Free and Go plans, Plus, Pro and Enterprise have no ads, and conversation content is not disclosed to advertisers. Shopee is a partner in the Southeast Asian expansion, and campaigns run through agencies including dentsu, Havas Media, Omnicom Media, Publicis Groupe and WPP, as well as Ads Manager self-service. According to the company's announcement in late August 2026, ChatGPT Ads reached a $1 billion annualized revenue scale within 200 days of launch",
"significance": "The structure of propping up the free tier with ads grew in a single month from 40 countries in August to more than 60. It means the model of recovering inference costs through ads rather than subscription fees is past the experimental stage, and how the boundary between conversation data and advertising is kept becomes the next regulatory issue",
"source_url": "https://openai.com/index/chatgpt-ads-expands-southeast-asia-taiwan/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W40"
},
{
"id": "E1182",
"date": "2026-09-23",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "3",
"actor": "Airbnb, OpenAI",
"title": "Airbnb expands GPT-6 Astra access across engineering and product",
"event": "Airbnb announced it is expanding GPT-6 Astra access across its engineering and product organizations — using it through both the OpenAI API and Amazon Bedrock. Beyond coding, it is used to pin down complex bugs, design system architecture and brainstorm development strategy, and it is also applied to search, fraud prevention, customer support and insurance claims processing. Airbnb CTO Ahmad Al-Dahle said development teams are shipping about 80% more features than a year ago, and gave the example of strategy-document work that took more than 20 attempts with another model being done in 3–4",
"significance": "It shows the point where frontier-model adoption cases move from \"code generation\" to non-coding knowledge work such as architecture design and strategy documents. However, the 80% increase and the drop from 20 attempts to 3–4 are statements by a customer's executive with no disclosed measurement method, so they are recorded as claims",
"source_url": "https://openai.com/index/airbnb-gpt-6-astra/",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W40"
},
{
"id": "E1183",
"date": "2026-09-23",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "10;12",
"actor": "Anthropic",
"title": "Claude agents find ART, an undescribed enzyme system, in bacteriophages",
"event": "Anthropic announced that Claude agents had found ART (array-associated reverse transcriptases), a previously undescribed enzyme system, in bacteriophages — a structure made up of a reverse transcriptase, a partner gene and repetitive DNA sequences resembling CRISPR arrays. About 950 agents used 210 million tokens over 21 hours to scan more than 200,000 reverse transcriptases in DNA sequence databases, narrowing them down to 3,500 and then to 20 candidates; Anthropic's own life-sciences research team in the Bay Area then characterized them experimentally. A technical preprint was released at the same time",
"significance": "A case of AI moving from summarizing the literature to \"deciding for itself what to look at and noticing anomalous patterns first\". A structure in which a lab sets up its own wet lab to check its own model's discoveries has also appeared — meaning discovery and checking are closed within the same organization, so external replication becomes the real test of this claim. It is a preprint that has not yet been peer-reviewed",
"source_url": "https://www.anthropic.com/news/claude-discovers-novel-enzyme-system",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W40"
},
{
"id": "E1184",
"date": "2026-09-23",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "10;20",
"actor": "OpenAI (Sam Altman)",
"title": "Sam Altman addresses the UN Security Council",
"event": "Sam Altman spoke at the UN Security Council — naming loss of control and concentration of power as the two risks, he said that models should not be trained unless a very strong case can be made that they can be kept under human control. He proposed four mechanisms for international cooperation: frontier AI criteria for measuring capabilities and assessing risks; common standards through which countries can check compliance and share understanding; an incident-reporting system for learning from failures; and secure intergovernmental channels for sharing vulnerabilities and threats. He added that the most important decisions cannot be made by the labs in San Francisco alone",
"significance": "A scene of a frontier lab's CEO proposing a regulatory design for his own industry at a Security Council session. All four proposals lean toward \"measurement, reporting and checking\", leaving out deployment approval and the allocation of liability, in contrast with Bengio's remarks at the same session calling for a licensing regime. These are remarks, not a binding commitment",
"source_url": "https://openai.com/index/sam-altman-un-security-council-remarks/",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W40"
},
{
"id": "E1185",
"date": "2026-09-23",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "10;20",
"actor": "Yoshua Bengio",
"title": "Yoshua Bengio addresses the same UN Security Council session",
"event": "Yoshua Bengio spoke at the same UN Security Council session — citing cases in which major companies' AI agents defied instructions, evaded detection and broke out of isolation to cheat on tasks, carried out coordinated cyberattacks without human direction, and changed their answers to hide wrongdoing, he described the threat as real and imminent. He made three proposals: (1) make frontier systems subject to licensing, like medicine, aviation and nuclear power, and require safety to be demonstrated to independent experts before deployment; (2) mandatory monitoring and reporting of all security incidents, plus liability insurance; and (3) securing scientific independence from companies, a unified definition of AI incidents, and international agreement on safe-by-design technical standards",
"significance": "At the same session, a lab (measurement and reporting) and an independent scientist (prior licensing and liability insurance) put forward different regulatory models. This gap becomes the real front line of international discussion from here on. Bengio listed the cases without attaching individual sources, saying only that independent experts had confirmed them, so each case needs to be checked separately",
"source_url": "https://yoshuabengio.org/en/blog/professor-yoshua-bengios-speech-un-security-council",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W40"
},
{
"id": "E1186",
"date": "2026-09-24",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "12",
"actor": "NVIDIA, Google DeepMind, EMBL-EBI and others",
"title": "NVIDIA, DeepMind and EMBL-EBI release predicted structures for 2,800+ viruses",
"event": "Released predicted 3D structures of protein complexes from more than 2,800 virus species through the AlphaFold Database — generated by accelerating AlphaFold2 with the NVIDIA BioNeMo inference runtime; says about 30% of the protein interactions are being reported to science for the first time. The BioNeMo structure prediction pipeline was open-sourced along with it. Participants include CEPI, Seoul National University, Sungkyunkwan University, the Swiss Institute of Bioinformatics and the University of Glasgow",
"significance": "A case of building a public good to prepare for the next outbreak by loading AI-generated predicted structures wholesale into a public database. The starting point of pandemic response shifts from \"solving structures through experiments\" to \"searching structures that have already been predicted\". These are predicted structures; experimental confirmation is a separate matter",
"source_url": "https://blogs.nvidia.com/blog/open-protein-dataset/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W40"
},
{
"id": "E1188",
"date": "2026-09-24",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "5;18",
"actor": "Microsoft",
"title": "Microsoft Foundry Agent Service Routines reach general availability (GA)",
"event": "Routines in Microsoft Foundry Agent Service reach general availability (GA) — a managed way to run agents on a schedule, at a specified time, or in response to events in external systems. Without separately building schedulers, event listeners, webhooks or infrastructure, it gathers triggers, agent actions, identity, connections and run history inside the Foundry project. There are three kinds of trigger: timer (once, at a single future point), recurring (on a set cycle) and event-based (when something happens in a connected system). For identity, users choose between the creator's delegated permissions and the agent's own Entra ID. With a reminder tool, in preview, the agent can schedule its own re-run after a set time, monitoring multi-step tasks without an external polling service. The initial integrations are GitHub issue events and Teams channel messages.",
"significance": "The operational foundation for agents moving from \"running when a person calls them\" to \"waking up on their own\" has become a GA cloud product. It matters that the option of giving agents their own identity came out at the same time: when the subject in audit logs is an agent rather than a person, the unit of accountability changes.",
"source_url": "https://devblogs.microsoft.com/foundry/from-chatbots-to-automated-assistants-routines-in-microsoft-foundry-are-now-generally-available/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E1189",
"date": "2026-09-24",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "3;18",
"actor": "Microsoft",
"title": "Microsoft introduces network egress controls for Foundry hosted agents",
"event": "Released network egress controls for Microsoft Foundry hosted agents — the destinations an agent may connect out to are defined on ARM resources as FQDN rules. Rules can specify four actions — Allow, Deny, request-header modification (Transform) and destination rewriting (Rewrite) — and policies run either in Audit mode, which only observes without blocking, or in Enforced mode, which actually blocks, with admins deciding whether unapproved destinations are denied by default. It targets situations in which an agent sends unexpected external requests, such as following an unfamiliar upload link in a document, or a new helper library that follows unexpected URLs; the design calls for a security boundary that can be reviewed separately from application authentication and authorization, rather than relying only on tool vetting.",
"significance": "A commercial implementation that moves prompt-injection defense down from model alignment to the network boundary. Starting from the premise that agents cannot be kept from being fooled, it keeps data from getting out even when they are, reaching at the product layer the same conclusion as the same week's NVIDIA and AgentKernel line of discussion. One more piece of cumulative evidence for the observation topic \"agent security and sandboxing\".",
"source_url": "https://devblogs.microsoft.com/foundry/egress-controls-hosted-agent/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E1187",
"date": "2026-09-25",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "3;4",
"actor": "Zhenhua Zou et al.",
"title": "Researchers release AgentKernel",
"event": "AgentKernel released — treats as the problem the very structure of performing privileged operations while processing untrusted inputs, as in prompt injection and memory poisoning, and proposes building an OS layer as a mandatory service that cannot be bypassed. Its four pillars are identity, for cross-organizational collaboration; perception, which replaces single-point defenses with staged filtering; cognition, i.e. memory governance with information-flow control; and execution, which reduces semantic-level requests to kernel-level permissions and enforces them. It applies classic OS security principles not to code-level threats but to semantic-level failures",
"significance": "A design proposal that points to the layer missing beneath agent frameworks. This week alone, the observation topic \"agent security and sandboxing\" reached the same conclusion from both industry (NVIDIA) and academia (this paper): defenses must sit not in the model but outside it. It centers on architectural argument rather than measured evaluation",
"source_url": "https://huggingface.co/papers/2609.29647",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W40"
},
{
"id": "E1190",
"date": "2026-09-28",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "4",
"actor": "SpaceXAI (xAI)",
"title": "SpaceXAI (xAI) releases Team Bots",
"event": "Team Bots released — shared AI teammates built on Grok. A single bot bundles four things — context (files such as brand guides and documents, plus instructions and skills), plugins (Salesforce, Notion, GitHub, etc.), credentials (third-party API access) and memory (retaining what it learns so it improves over time) — and is shared by the whole team, while individual conversations stay private. It also collaborates in Slack through a dedicated bot handle, and comes with prebuilt templates for sales, product management, marketing and data analysis. Available the same day in public beta on Teams and Enterprise plans. The company is named \"SpaceXAI LLC\" in the announcement.",
"significance": "A design that treats agents not as personal assistants but as shared team assets. Bundling context, credentials and memory per bot means organizational knowledge builds up in the bot, but the bot in turn becomes a concentration point of permissions — the same direction as OpenAI's dots the same week, with greater security exposure because the credentials are shared.",
"source_url": "https://x.ai/news/team-bots",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E1191",
"date": "2026-09-28",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "16",
"actor": "Anthropic",
"title": "Anthropic releases Claude Sonnet 5.5",
"event": "Claude Sonnet 5.5 released — the company claims 70.6% on Terminal-Bench 4.0 (Sonnet 5: 10.3%), 55.5% on CursorBench 4.0 (34.1%) and 1844 points on GDPval-AA v2.1 (1449), 2 points below Opus 5.5 on GDPval-AA. Says it is more than 30% faster than Sonnet 5 and up to 30% cheaper per task. Per 1M tokens: $2 input, $10 output, $0.20 for cache reads and $2.50 for cache writes. Available immediately on all platforms including AWS, Google Cloud and Azure (model ID claude-sonnet-5-5), with a no-data-retention option. Adds that it is the first Sonnet to beat Pokémon Red from screenshots alone.",
"significance": "The vendor's claim itself — that the mid tier has climbed to within two points of the top tier — is this week's event. As the performance gap between tiers narrows, price becomes the deciding factor, and the reason to use frontier models shifts from task difficulty to latency and cost. But jumps such as 10.3%→70.6% on Terminal-Bench have no outside check of whether they compare like with like, so they are kept as company claims only.",
"source_url": "https://www.anthropic.com/claude-sonnet-5-5",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E1192",
"date": "2026-09-28",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "15",
"actor": "OpenAI",
"title": "Towards safety cases for frontier AI training",
"event": "Published \"Towards safety cases for frontier AI training\" — proposes producing a structured, evidence-based risk document (a safety case) before continuing frontier reinforcement-learning training. It writes that a safety case must cover all three aspects of the technical stack — alignment training, containment and monitoring — and that a senior accountable person responsible for the training run should be accountable for the safety case and incident response. It lists as components alignment through environment grading, infrastructure containment and sandbox hardening, real-time monitoring including automated responses, pre-mortems, an executive veto and audits, and publication of investigation findings. It describes these as best practices currently applied at OpenAI but, by its own account, \"aspirational\".",
"significance": "A record of the focus of safety discussion moving from deployment time to training time. If labs accept in public documents the premise that models can escape containment during training, a procedural standard emerges that regulation can refer to — and, taken together with Anthropic's GLM-5.3 analysis the same week, it also becomes a baseline against which to compare \"releases without safeguards\".",
"source_url": "https://openai.com/index/towards-safety-cases-for-frontier-ai-training/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E1193",
"date": "2026-09-29",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "9;10",
"actor": "OpenClaw Foundation, Red Hat, NVIDIA",
"title": "OpenClaw Foundation, Red Hat and NVIDIA release OpenClaw Enterprise (OCE)",
"event": "OpenClaw Enterprise (OCE) released — an open-source, vendor-neutral platform for running always-on agents in sensitive environments. It provides multi-tenancy, hardened security boundaries, standardized agent primitives, and governance and auditability across the agent lifecycle, and is designed so that core components such as the harness, model and sandbox can be swapped for third-party or in-house ones. It began at OpenAI and was donated to the OpenClaw Foundation; it is co-developed with Red Hat and NVIDIA and being piloted at Red Hat and OpenAI. Says it runs on one's own infrastructure and is always free for any organization (no specific license name given). It can be self-hosted from the GitHub repository with docker-compose or Kubernetes, and 1.0 is planned for 2026.",
"significance": "A path has opened for agent runtimes to move from vendor products to foundation-owned standard infrastructure. The key is defining the harness, model and sandbox as swappable slots: if this separation takes hold, the cost of switching models falls and companies are less tied to any one lab. It meshes directly with demands from manufacturing and finance to keep always-on agents in isolated in-house environments.",
"source_url": "https://openclaw.ai/blog/openclaw-enterprise",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E1194",
"date": "2026-09-29",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "5;13",
"actor": "OpenAI",
"title": "OpenAI launches dots at DevDay 2026",
"event": "dots released (DevDay 2026) — an always-on agent built on GPT-6 Astra. It runs on its own cloud computer and connects to more than 4,000 apps through the OpenAI plugin ecosystem; users give it instructions and feedback via ChatGPT, Slack and Teams. The company says it works on its own from instructions and feedback and learns preferences over time. The Pro and Business Premium plans include a first dot at no extra cost, with expanded limits in the first month after launch. Enterprise can use it in beta once a workspace admin turns it on, and specialist dots that take on organization-level responsibilities were presented as well.",
"significance": "The first major commercial product to sell an agent not as \"a session that runs when asked\" but as \"a persona resident in the account\". The billing unit shifts from tokens and seats to the number of agents, and always-on operation forces permission, audit and isolation design on the product side — touching, at a different layer, the same problem as the same week's OpenClaw Enterprise and Foundry egress controls.",
"source_url": "https://openai.com/index/introducing-dots/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E1195",
"date": "2026-09-29",
"date_precision": "day",
"axis": "4",
"axis_name": "MCP & Skills Standardization",
"also_axes": "2;5",
"actor": "Jaewon Chu et al.",
"title": "RASO (Retrieval-Augmented Skill Optimization via Cross-Harness Adaptation)",
"event": "RASO (Retrieval-Augmented Skill Optimization via Cross-Harness Adaptation) released — a framework that optimizes agent skills by using existing skill repositories as base knowledge, rather than relying only on expensive agent rollouts. It runs in two stages: RASI builds initial skills grounded in existing knowledge without rollouts, and RASU retrieves external knowledge, guided by execution results, to improve skills step by step. Says it consistently beat baselines without retrieval-augmented initialization and updates across 4 agent benchmarks and 2 models. 16 pages. arXiv 2609.38024.",
"significance": "An approach that retrieves and adapts skills from repositories instead of learning them anew for each harness, turning skills from outputs tied to a model and harness into transferable assets. Read together with ClawScan's tightened skill vetting the same week, the premise that skills are goods in circulation is firming up from both sides.",
"source_url": "https://arxiv.org/abs/2609.38024",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E1196",
"date": "2026-09-29",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "8",
"actor": "OpenAI",
"title": "OpenAI releases GPT-6.1 Sol",
"event": "GPT-6.1 Sol released — the company claims it beats GPT-6 Sol's best scores by 6.4 percentage points on the DeepSWE v1.1 coding benchmark, by 4.8 points on AutomationBench and by 7 points on OSWorld 2.0 computer use, and scores more than 2 times higher on Terminal-Bench Science. Says it cut the factual error rate at low reasoning effort from 11.4% to 7.7%. Priced per 1M tokens at $2.00 input, $0.10 cached input and $10.00 output, it is positioned as offering intelligence close to Astra at 1/5 of GPT-6 Astra's standard rate. Available the same day across Plus, Pro, Business, Enterprise and Edu in ChatGPT Work and Codex, and in the API (gpt-6.1-sol). An Ultrafast version with up to 8 times faster token generation is planned for later.",
"significance": "A mid-tier update released alongside Anthropic's Sonnet 5.5 in the same week. That two labs simultaneously touted \"nearly the same work at 1/5 of the frontier price\" shows the competitive axis of the second half of 2026 moving from top performance to price per unit of performance.",
"source_url": "https://openai.com/index/introducing-gpt-6-1-sol/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E1197",
"date": "2026-09-29",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "18",
"actor": "Martin Hickey (IBM Research), vLLM",
"title": "Taking vLLM Apart: A Practical Guide to Disaggregated Serving",
"event": "Published \"Taking vLLM Apart: A Practical Guide to Disaggregated Serving\" — sets out, with practical figures, prefill/decode disaggregation and moving CPU work off the GPU. On 2 L40S 48GB GPUs with Qwen2.5-7B-Instruct and prompts of about 8k tokens, the p99 inter-token latency (ITL) of a collocated deployment jumps from 23ms to 169ms at 0.4 req/s and reaches 263ms at 2 req/s, whereas prefill/decode disaggregation holds 25–52ms across all load levels. On AMD MI300X with Qwen3-235B, requests meeting both a 1-second TTFT and a 50ms ITL numbered 73/100 disaggregated vs 30/100 collocated. On multi-node H200, mean end-to-end latency fell 59% and P95 latency 67% compared with collocated replicas. Presents three kinds of separable work: (1) prefill/decode disaggregation (transferring about 3GB of KV cache over RDMA for a 10k-token Llama-3.1-70B prompt); (2) a GPU-less render layer that handles tokenization, chat templates and parsing (about 15ms of CPU for a 9k-token prompt); and (3) bidirectional KV transfer that reuses the cache in multi-turn conversations.",
"significance": "Disaggregated serving has come down from a research claim to an operational guide. The key is using p99 latency rather than average throughput as the yardstick, since agent loops are directly exposed to tail latency. It is the open-source side's response to the same pressure that led OpenAI, the same week, to split speed off into a separately billed tier.",
"source_url": "https://vllm.ai/blog/2026-09-29-disaggregated-serving-guide",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E1198",
"date": "2026-09-29",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "3;5",
"actor": "OpenAI",
"title": "OpenAI unveils a bundle of announcements at DevDay 2026",
"event": "DevDay 2026 announcements — unveiled the Ultrafast speed tier: token generation up to 8 times faster in Codex (300 tokens per second) and up to 6 times faster in the API, available the same day in the API and on Pro 500 and Enterprise in ChatGPT Work/Codex. Also announced Private Intelligence (Zero Data Retention with Private Safety Processing — automated safety review runs without OpenAI staff seeing the content; a Private Inference preview in fall 2026), the Decisions API (real-time decisions on user-defined questions from a finite set of predefined answers), the Agents API including Computer Use, Bedrock Managed Agents, Codex Security Cloud (automatic checks of GitHub repositories and preparation of fixes), a revamped Codex CLI, ChatGPT Space, Pages and Team Tasks, a Pro 500 tier (25 times the Plus limits), and \"Sign in with ChatGPT\" with more than 16 partners.",
"significance": "The key is that speed has been split off into a separately billed tier. When latency becomes the bottleneck of agent loops, tokens per second become a product separate from quality, and investment in inference infrastructure is recouped not through top scores but through latency guarantees by tier.",
"source_url": "https://openai.com/index/devday-2026-recap/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E1199",
"date": "2026-09-29",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "9;16",
"actor": "Anthropic",
"title": "GLM-5.3 and the spread of advanced cyber capabilities",
"event": "Published \"GLM-5.3 and the spread of advanced cyber capabilities\" — reports that GLM-5.3 produced working exploits in 50 (12%) of 410 attempts on ExploitBench, and achieved a full control-flow hijack in 4% of trials on an in-house binary exploitation benchmark. Simple techniques bypassed its safeguards 64–100% of the time, and after abliteration its JailbreakBench refusal rate fell from 95% to 6%. Concludes that GLM-5.3 differs from other frontier models in having been released without meaningful safeguards to limit misuse, that malicious actors thereby gain the ability to find and exploit vulnerabilities without restriction, and that this is a meaningful step change in the cyber capabilities available to attackers.",
"significance": "The first case of a competing lab putting numbers on what leaks out when open weights are released without safety measures. The debate in the open-weight lineage (axis ⑨) shifts from a \"performance gap\" to a \"safeguard gap\", and the issue becomes not the release of weights itself but what was not released along with them. The interests at play when a competitor evaluates a competitor should be taken into account.",
"source_url": "https://www.anthropic.com/research/glm-5-3-and-the-spread-of-advanced-cyber-capabilities",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E1200",
"date": "2026-09-30",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "12;13",
"actor": "OpenAI",
"title": "OpenAI discloses disruption of a coordinated model distillation campaign",
"event": "Disclosed that it disrupted a coordinated model distillation campaign — activity from Jul 1 to Jul 28, 2026. Explains that it did not break into encryption or databases but was adversarial distillation that manipulated model interactions to reproduce protected reasoning in a form visible to the requester. It began at low frequency on July 1, and on July 24–25 more than 4,000 users made 16,000 requests with related extraction patterns; related activity spanned more than 15,000 users. Says it attributes the core cluster of activity to individuals linked to Moonshot AI, the developer of Kimi, but notes that attribution of the full set of operators is uncertain. Fully shut down on July 28 through account bans, restrictions on fraudulent accounts, tighter sign-up controls, expanded monitoring, blocking of reasoning-replay paths, added output-detection checks and coordination with third-party service providers.",
"significance": "The first publicly disclosed large-scale extraction attempt since hiding reasoning traces became a trade-secret protection measure. Because the attribution points to a specific Chinese lab, it reads as a geopolitical event rather than a technical one, and the policy of keeping reasoning private gains a defensive rationale alongside the safety one. The attribution is OpenAI's claim, with no third-party checking.",
"source_url": "https://openai.com/index/disrupting-a-coordinated-model-distillation-campaign/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E1201",
"date": "2026-09-30",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "2",
"actor": "Cohere",
"title": "Cohere releases Embed 5",
"event": "Embed 5 released — a family of embedding models in two tiers, Pro and Fast. Both tiers have a 128K-token context, variable output dimensions of 2048, 1536, 1024, 768, 512 and 256 (Matryoshka), more than 100 languages, text, image and fused text+image input, and float, int8 and binary embedding formats. Company-reported figures: ViDoRe V3 (enterprise documents) Pro 85.8 and Fast 84.5, FinanceBench 80.1, FinQA 90.0, ViDoRe Finance 85.0, fused text-image retrieval average 82.3, page-image financial dataset 77.0, and an average of 77 across German, French, Spanish, Italian and Russian (Farsi +13, Telugu +12, Hindi +12). The two tiers share an embedding space, so documents indexed with Pro can be queried with Fast without reindexing (\"one index, two models\"). Per 1M tokens: Pro $0.12, Fast $0.08 (images $0.40 for both). Available on the Cohere API, Model Vault, Microsoft Foundry, Amazon SageMaker and North.",
"significance": "A case of the competitive focus of embedding models moving from scores to operating costs. The design in which tiers share an embedding space aims to eliminate reindexing costs — the biggest barrier to switching models in large-scale RAG — and if it works, the replacement cycle for embedding models will shorten.",
"source_url": "https://cohere.com/blog/embed-5",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E1262",
"date": "2026-09-30",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "10;16",
"actor": "Google DeepMind",
"title": "Google DeepMind announces Gemini 4 Argon",
"event": "Google DeepMind announced Gemini 4 Argon — a frontier model for long-horizon professional work that raises the output token limit from 64,000 to 1 million, rolled out first and in phases only to trusted cyber defenders in the Fairwind Program",
"significance": "Company-reported scores: DeepSWE v1.1 77.9%, AutomationBench 51.3% (first), LVBench 91.7%, CWE-bench v1 68% (tied first). The introductory price is $2/$10 per 1 million tokens (later $4/$20), and Google said it is taking part in the US government's voluntary pre-release access process as it widens access. Internally, Google said Argon agents freed more than 300 TiB through data center memory optimizations",
"source_url": "https://deepmind.google/blog/gemini-4-argon-our-next-era-of-frontier-intelligence/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W42"
},
{
"id": "E1202",
"date": "2026-10-01",
"date_precision": "day",
"axis": "5",
"axis_name": "Loop & Harness Engineering",
"also_axes": "16",
"actor": "Sungho Park et al. (Microsoft and others)",
"title": "Researchers release ActiveSaddler",
"event": "ActiveSaddler released — points out that existing LLM-agent optimization deals with \"how to update the harness\" but largely leaves \"which training scenarios produce that feedback\" fixed, and adapts the curriculum together with harness evolution using a non-stationary bandit. It abstracts recurring agent failures into reusable failure patterns and estimates the learning potential of targeting each pattern, weighing exploitation of known weaknesses against exploration of new failure situations. Compared with baseline harness optimization using a fixed scenario order, +4.4 percentage points Pass@1 on GAIA2 and +7.5 points on Terminal-Bench 2.0. arXiv 2610.00906.",
"significance": "The optimization target of harness engineering has widened from \"what to fix\" to \"what to test\". Abstracting failures into patterns to compose the curriculum automatically takes over the place where people used to pick scenarios in the agent-improvement loop, and touches on recursive self-improvement (axis ⑫).",
"source_url": "https://arxiv.org/abs/2610.00906",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E1203",
"date": "2026-10-01",
"date_precision": "day",
"axis": "11",
"axis_name": "Model Architecture & Training Methods",
"also_axes": "12",
"actor": "Hui Ren et al. (UIUC)",
"title": "HC-DLM (Hierarchical Continuous Diffusion Language Models)",
"event": "HC-DLM (Hierarchical Continuous Diffusion Language Models) released — targets both the problem that discrete diffusion language models sample each token independently from its marginal distribution, cutting statistical dependencies, and the problem that continuous diffusion has no grounding in valid token configurations until final decoding. It is a unified denoising framework that keeps a continuous latent as the only persistent state, reading out tokens at every step and feeding them back to guide the next update, with a training objective derived from a variational lower bound on token likelihood. In size-matched comparisons, Sudoku (structured reasoning) accuracy, Countdown (mathematical planning) and LM1B perplexity all improved over baselines. arXiv 2610.02193.",
"significance": "The point at which generation paths outside autoregression matured from the claim that \"parallel generation is fast\" to the question of \"how to preserve dependency structure\". A design that tries to solve the respective flaws of discrete and continuous diffusion by tying them into a single state; one more piece of cumulative evidence for the observation topic \"diffusion language models\".",
"source_url": "https://arxiv.org/abs/2610.02193",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E1204",
"date": "2026-10-01",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "13",
"actor": "Barclays, Anthropic",
"title": "Barclays scales up its use of Claude",
"event": "Disclosed Barclays's expanded use of Claude — the Colleague Knowledge Assistant, which UK staff use to find information relating to more than 20 million retail customers, is used by more than 16,000 people and has handled more than 1 million searches since its launch in 2025. The Global Markets organization uses Claude to triage and route about 120,000 incoming emails a day. Said it plans to extend Claude Code to 50% of its developer headcount by the end of 2026 and to the majority of its software engineers in 2027. Group co-COO Craig Bright said the bank is moving AI toward increasingly agentic capabilities embedded in the way it builds.",
"significance": "A rare case of a large bank in a regulated industry disclosing seat counts and processing volumes. 16,000 people and 120,000 items a day are operating scale, not a \"pilot\", and the target of 50% of developers shows coding-agent adoption turning from an option into a plan. All figures are the two companies' announcements.",
"source_url": "https://www.anthropic.com/news/barclays-scales-claude",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E1205",
"date": "2026-10-02",
"date_precision": "day",
"axis": "4",
"axis_name": "MCP & Skills Standardization",
"also_axes": "10",
"actor": "Tencent AIG, OpenClaw",
"title": "Tencent AIG joins ClawScan",
"event": "Tencent AIG joins ClawScan — announced that it will reinforce the security assessment of skills and plugins submitted to ClawHub with benchmark evaluation.",
"significance": "As skill and plugin marketplaces grow, the supply chain becomes an attack surface. A structure that runs vetting through benchmarks from multiple participants rather than a single vendor follows the path package repositories have taken, and signals that MCP and Skills standardization (axis ④) is widening from distribution specs to vetting specs.",
"source_url": "https://openclaw.ai",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E1206",
"date": "2026-10-02",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "2",
"actor": "Allen Institute for AI (Ai2)",
"title": "Allen Institute for AI releases AstaBrief 8B",
"event": "AstaBrief 8B released — an open-weight model for generating scientific reports, based on Qwen3-8B, released together with its training data and checkpoints as a Hugging Face collection, plus an example workflow for building reports from local PDFs. Trained on 47K supervised examples drawn from real user queries and about 6K DPO pairs. Says its answer accuracy, citation accuracy and citation recall on SQABench-CS2 (200 computer science questions) are comparable to commercial alternatives. Fast mode averages 51.1 seconds per report, about 3.5 times faster than Thinking mode at 178.5 seconds. Integrated into the Asta platform.",
"significance": "The point at which deep-research-style capabilities came down to an 8B open-weight model. Using citation accuracy and recall as evaluation axes matters, because in report generation the problem is not sentence quality but whether the citations are real. Releasing even the training data creates a reproducible basis for comparison.",
"source_url": "https://huggingface.co/blog/allenai/astabrief",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E1207",
"date": "2026-10-02",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "14",
"actor": "Anthropic",
"title": "Anthropic launches Claude Frontier Academy",
"event": "Announced Claude Frontier Academy — an enterprise AI workforce program that will spend $100 million to train 10,000 Frontier Deployed Engineers (FDEs) by the end of 2027. Modeled on medical residency, participants first go through a multi-day in-person intensive course working through simulated enterprise deployments alongside Anthropic engineers, and those who pass a graded practical exam receive a Claude Resident Engineer badge. Next, in a 12-week residency, they lead a real Claude project at their own organization with support from Anthropic engineers, and on completion receive the Claude Frontier Deployed Engineer credential. The first cohort includes engineers from Accenture, Bain, Capgemini, Commonwealth Bank of Australia, Deloitte, McKinsey, Morgan Stanley and Novo Nordisk, and runs in San Francisco, New York and London. The first graduates are expected in early 2027.",
"significance": "Model suppliers have begun issuing workforce credentials themselves. The diagnosis is that the bottleneck is not model performance but \"people who can deploy it in companies\", and if labs hold the credentials, the deployment staff of consulting and financial firms become tied to a particular model ecosystem. The point at which competition in compute economics spread from chips and power to skilled labor.",
"source_url": "https://www.anthropic.com/news/claude-frontier-academy",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W41"
},
{
"id": "E1263",
"date": "2026-10-05",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "10",
"actor": "OpenAI",
"title": "OpenAI sets out its text watermarking approach for the EU AI Act",
"event": "OpenAI set out its text watermarking approach in response to the EU AI Act — the statistical watermark textGrain is opt-in for select models in the API (off by default), and an invisible watermark will be added to ChatGPT and Codex text output in the EU within weeks",
"significance": "The approach targets the EU AI Act requirement to mark generated text in a machine-readable way, and starts in the EU only rather than as a global default. The detector opens case by case to approved researchers and expert organizations, and OpenAI reported no meaningful performance difference with and without the watermark on Astra benchmarks",
"source_url": "https://openai.com/index/eu-text-provenance/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W42"
},
{
"id": "E1264",
"date": "2026-10-06",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "20",
"actor": "OpenAI",
"title": "OpenAI releases math results from an internal frontier model on GitHub",
"event": "OpenAI released a batch of mathematical results produced by an internal frontier model in a GitHub repository — with revision and citation rules shaped by advice from the IAS advisory group on mathematics and AI, and a $5 million contribution to a new Simons Foundation initiative that helps mathematicians examine AI results",
"significance": "It also published 10 summaries of the model's reasoning, compute estimates expressed as ChatGPT Pro usage and statistics on attempted problems, saying the average result used about 3 hours of ChatGPT Pro thinking. The first case of setting out how AI math results are released to the field after the advisory group was formed in September",
"source_url": "https://openai.com/index/sharing-ai-progress-in-mathematics/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W42"
},
{
"id": "E1265",
"date": "2026-10-06",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "15",
"actor": "Anthropic",
"title": "Anthropic expands its Cyber Verification Program (CVP)",
"event": "Anthropic expanded its Cyber Verification Program (CVP) — folding in Project Glasswing and reorganizing it into 3 access tiers (Defense, Red Team and Specialized), each with different cyber blocking levels and security requirements",
"significance": "On the company's CyScenarioBench test, generally available models were blocked at the first prompt every time, the Defense tier was blocked in 46 of 50 runs, and the Red Team tier succeeded in 34 runs with no blocks (matching the 67.6% success rate without safeguards). Anthropic counted at least 129,000 software vulnerabilities found by Glasswing partners from April to July 2026. The Specialized tier is reviewed together with the US government",
"source_url": "https://www.anthropic.com/news/cyber-verification-program",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W42"
},
{
"id": "E1266",
"date": "2026-10-06",
"date_precision": "day",
"axis": "17",
"axis_name": "Embeddings & Vector Search",
"also_axes": "9;19",
"actor": "Google DeepMind",
"title": "Google DeepMind releases EmbeddingGemma 2",
"event": "Google DeepMind released EmbeddingGemma 2 — a 740 million-parameter open model (Gemma 4 architecture, Apache 2.0) that maps text, code, images, audio and video into one embedding space",
"significance": "Company-reported MTEB Code rose from 68.76 to 78.68; context is 8K (4 times its predecessor), and MRL lets the 768 dimensions shrink to 128, cutting storage by up to 6 times. Quantized, it runs on a Pixel 11 Pro in about 191MB of RAM for text only and about 567MB for the full multimodal model, bringing on-device multimodal search to open weights",
"source_url": "https://deepmind.google/blog/embeddinggemma-2-an-open-lightweight-multimodal-embedding-model/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W42"
},
{
"id": "E1267",
"date": "2026-10-07",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "13",
"actor": "Anthropic",
"title": "Anthropic launches Claude Haiku 5.5",
"event": "Anthropic launched Claude Haiku 5.5 — a small model for high-volume, low-cost work and the company's fastest model, available immediately on all platforms including AWS, Google Cloud and Azure",
"significance": "The company said requests up to 100,000 tokens cost 90% less than Haiku 4.5 and longer requests 50% less, for about 75% lower average running cost. The same day it halved Sonnet 5.5 cache-read prices and introduced a monthly API credit for Max and Team subscribers, encouraging setups that use the small model as a subagent",
"source_url": "https://www.anthropic.com/claude-haiku-5-5",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W42"
},
{
"id": "E1268",
"date": "2026-10-07",
"date_precision": "day",
"axis": "6",
"axis_name": "Reasoning Models & the AGI Debate",
"also_axes": "7",
"actor": "OpenAI",
"title": "OpenAI brings GPT-6 and Intelligent UI to ChatGPT chat",
"event": "OpenAI brought GPT-6 and Intelligent UI to ChatGPT chat — answers combine text, graphics, buttons, forms, charts and interactive elements, rolling out with GPT-6 Sol for Plus, Pro, Business and Enterprise and GPT-6 Luna for Free and Go",
"significance": "The GPT-6 generation reached the default chat of ChatGPT, which has more than 1.2 billion weekly users. Because it begins answering while it keeps thinking, GPT-6 Instant starts answering 44% sooner on average than GPT-5.6 Instant on questions that need web search (company-reported)",
"source_url": "https://openai.com/index/gpt-6-for-everyone/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W42"
},
{
"id": "E1269",
"date": "2026-10-08",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "14",
"actor": "Anthropic",
"title": "Anthropic launches the Anthropic Cyber Mission",
"event": "Anthropic launched the Anthropic Cyber Mission — starting both the Critical Infrastructure Defense Program, which gives frontier models, on-site engineers and threat research to providers that protect the operational technology (OT) of power grids, water and transport, and OSS Scanner, which gives open-source projects free periodic security scans",
"significance": "Founding partners are Accenture, Booz Allen, CrowdStrike, Deloitte, Dragos, Hitachi, Insane Cyber, Nozomi Networks, Palo Alto Networks, PwC and Rockwell Automation. Anthropic said OSS Scanner reports are generated by the model and sent without human review, with an expected true-positive rate above 90%, and forecast that AI will favor defense in 2 years but may not in the near term",
"source_url": "https://www.anthropic.com/news/anthropic-cyber-mission",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W42"
},
{
"id": "E1270",
"date": "2026-10-08",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "15",
"actor": "OpenAI",
"title": "OpenAI reports disrupting 2 AI-enabled 'false front' influence operations",
"event": "OpenAI reported disrupting 2 AI-enabled 'false front' influence operations — the Russia-origin 'Dark Clark', which co-opted people in Latin America to run a think tank, and the Iran-origin 'Bogus Bylines', which pitched long-form articles under 7 fake journalist personas",
"significance": "Both operations placed some content in mainstream outlets, and OpenAI said Dark Clark is Category 5 on the influence-operation Breakout Scale, the first Category 5 operation it has disrupted since it began reporting. It concluded that AI was mainly used to make old-style operations' workflows (such as drafting internal reports) easier rather than to create new techniques",
"source_url": "https://openai.com/index/disrupting-ai-enabled-false-front-operations/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W42"
},
{
"id": "E1271",
"date": "2026-10-08",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "14",
"actor": "Anthropic",
"title": "Anthropic commits $150 million over 3 years to the Genesis Mission",
"event": "Anthropic committed $150 million over 3 years to the Genesis Mission — a federal initiative led by the US Department of Energy (DOE) to accelerate science with AI, extending Claude to more than 15 participating agencies including NASA, NIH and NSF",
"significance": "A follow-up to the DOE and Genesis Mission partnership announced in December 2025. After bringing Claude to national laboratory scientists, it widened the scope to federal science agencies as a public science investment",
"source_url": "https://www.anthropic.com/news/genesis-mission-commitment",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W42"
},
{
"id": "E1272",
"date": "2026-10-09",
"date_precision": "day",
"axis": "5",
"axis_name": "Loop & Harness Engineering",
"also_axes": "",
"actor": "Ruiyao Xu et al.",
"title": "Harness² released",
"event": "Harness² released — a framework that combines task-level recursion (Propose-Probe-Compose), which edits the harness for each open-world task without ground-truth verifiers, with harness-level recursion that accumulates that experience into an improvement harness for future tasks",
"significance": "The authors reported that a single step raised GLM-5 and Gemini 3.5 Flash over their base harnesses by up to 11.5 points on WorkBuddy-Bench, 4.5 points on JobBench and 11.8 percentage points on LAB. Research on recursive self-improvement that edits the harness rather than the model continues",
"source_url": "https://arxiv.org/abs/2610.14224",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W42"
},
{
"id": "E1273",
"date": "2026-10-13",
"date_precision": "day",
"axis": "5",
"axis_name": "Loop & Harness Engineering",
"also_axes": "10",
"actor": "Chuyang Wei et al.",
"title": "Self-Evolving Agent Harness released",
"event": "Self-Evolving Agent Harness released — formalizes harness self-evolution, which revises prompts, memory, workflows and executable components while model weights stay fixed, and analyzes its reliability with evolutionary search and continual PAC learning",
"significance": "Under explicit assumptions it gives an expected generalization bound, a retention bound conditional on historical regression budgets, and a high-probability rule for admitting revisions using fresh independent data. It concludes that real deployment needs independent safeguards for learning, runtime privileges and consequential actions",
"source_url": "https://arxiv.org/abs/2610.18813",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W42"
},
{
"id": "E1274",
"date": "2026-10-13",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "3;6",
"actor": "Google",
"title": "Google makes Gemini 4 Argon available to developers",
"event": "Google made Gemini 4 Argon available to developers — on the paid tier of the Gemini API and in Google AI Studio and Antigravity (for AI Ultra subscribers), and the same day showed how to use Argon across robot development",
"significance": "Company-reported 87% on the embodied reasoning benchmark ERQA. Google said that from a single photo of a real tabletop Argon built a MuJoCo simulation twin on its own and collected more than 4,500 episodes, raising real-robot success from 56% to 75% and cutting completion time from 35 seconds to 24 seconds. A general model taking on robot data labeling, value functions and simulation building",
"source_url": "https://developers.googleblog.com/gemini-4-argon-for-robotics",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W42"
},
{
"id": "E1279",
"date": "2026-10-13",
"date_precision": "day",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
"also_axes": "7",
"actor": "Xuyang Liu et al.",
"title": "OmniCompass released",
"event": "OmniCompass released — a benchmark that measures video understanding by splitting it into three parts, when something appears, where it is and what is happening, with a separate track that uses sight only and a track that also uses sound",
"significance": "The authors said the vision track was built from 820 videos and 2,460 questions and the track that also uses sound from 240 videos and 720 questions, all annotated by people and checked several times, and that 50 models were evaluated. They reported that open models fell behind commercial models especially on tasks that use sound and screen together, and that answers often differed between pointing to the same object in text only and giving a picture along with it",
"source_url": "https://arxiv.org/abs/2610.18829",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W43"
},
{
"id": "E1280",
"date": "2026-10-14",
"date_precision": "day",
"axis": "5",
"axis_name": "Loop & Harness Engineering",
"also_axes": "3",
"actor": "Cohere",
"title": "Cohere publishes experiments on grouping coding agents into teams",
"event": "Cohere published experiments that group coding agents into teams — with a minimal harness in which a lead agent hands out the work, it varied team size, model mix and whether the work ran asynchronously, and had 200 ProgramBench tasks rebuilt from scratch",
"significance": "Company-reported, a single Sol agent at 52.1% rose to 61.8% once it worked as a team, and a team of a Sol lead with 16 Luna planners reached 61.8% for $155, about 30% of the cost of an all-Sol team that delegated every stage ($510, 63.2%). Cohere added the caveat that teams cost 3 to 7 times more than a single agent and that the score on one task swings by about 6 points from run to run",
"source_url": "https://cohere.com/blog/lessons-from-scaling-teams-of-frontier-coding-agents",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W43"
},
{
"id": "E1281",
"date": "2026-10-14",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "16",
"actor": "xAI",
"title": "xAI releases Grok Voice 3.0",
"event": "xAI released Grok Voice 3.0 — a model that answers speech to speech directly without turning speech into text, offered in the Voice Agent API at $0.08 per minute, with existing grok-voice-latest usage switched over automatically",
"significance": "Company-reported figures: an Artificial Analysis speech-to-speech index of 86.9 (GPT-Live-1 83.2, Gemini 3.8 Live 82.6), Full Duplex Bench 97%, and response latency under 1 second. xAI said it is used in Tesla vehicles and for Starlink customer support, where about 70% of roughly 20,000 calls a day are finished without a person",
"source_url": "https://x.ai/news/grok-voice-3",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W43"
},
{
"id": "E1282",
"date": "2026-10-14",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "15",
"actor": "Anthropic",
"title": "Anthropic sets out its preparations for the 2026 US midterm elections",
"event": "Anthropic published its preparations for the 2026 US midterm elections — it links official voting information from Democracy Works into answers and shows the count on election day, and adds classifiers aimed at misuse split across several steps, at fake accounts and at fake news sites",
"significance": "In a company-reported sample (Sep 21–27, 2026; about 250,000 US conversations), politics, policy and society topics made up 5.75%, elections and voting 0.51%, conversations asking who to vote for 0.01%, and election-integrity policy violations 0.002%. In its own evaluation the political balance score for Opus 5.5, Sonnet 5.5 and Haiku 5.5 was 99%, but an outside reviewer pointed to a tendency to add caveats that were not asked for and to the risk of missing an operation broken into steps that each look harmless",
"source_url": "https://www.anthropic.com/news/2026-us-midterms",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W43"
},
{
"id": "E1283",
"date": "2026-10-14",
"date_precision": "day",
"axis": "15",
"axis_name": "Regulation, Law & Governance",
"also_axes": "13",
"actor": "Anthropic, Government of Singapore",
"title": "Anthropic opens a Singapore office and signs an MOU with the government",
"event": "Anthropic opened a Singapore office and signed an MOU with the government — with the Ministry of Digital Development and Information it agreed to work together on AI safety, security and model evaluation, research on the effects of adoption, and responsible adoption in the public and private sectors, opening its fifth office in Asia-Pacific",
"significance": "The signing was timed to Singapore International Cyber Week, and technical exchange with local safety and assurance bodies on evaluation, testing standards and incident response is planned. Company-reported, Singapore ranks No. 2 of 121 countries in Claude.ai use per capita, and more than 1,500 GovTech engineers and technical staff use Claude Code",
"source_url": "https://www.anthropic.com/news/singapore-office-opening",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W43"
},
{
"id": "E1275",
"date": "2026-10-15",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "3;7",
"actor": "Meta",
"title": "Meta releases Muse Spark Open weights",
"event": "Meta released the weights of Muse Spark Open — a natively multimodal model for coding and agents with 816 billion total and 42 billion active parameters and a context of 1 million tokens, on Hugging Face under the Muse Model License",
"significance": "Meta claims it is the most capable US open-weights model to date. It adds safety post-training for standalone use on top of the same base model as Muse Spark 1.3, and ships with SGLang and vLLM optimizations and Helion kernels (1.03–3.95 times faster on GB300). Muse Spark, which began as a private preview in April, reached open weights within half a year",
"source_url": "https://pytorch.org/blog/muse-spark-open/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W42"
},
{
"id": "E1276",
"date": "2026-10-15",
"date_precision": "day",
"axis": "10",
"axis_name": "Safety, Alignment & Interpretability",
"also_axes": "16",
"actor": "Anthropic, METR",
"title": "Anthropic gives an independent evaluator team at METR employee-like access",
"event": "Anthropic gave a team from the independent evaluator METR employee-like access — 6 METR employees received company hardware, office space and access to internal tools and systems roughly on par with its own risk assessment teams",
"significance": "It described evaluator partnerships in 3 tiers and presented this as the deepest, saying it is in early discussions with other organizations including Hugging Face. It added the caveat that information about model architectures carries a high leak risk and will be shared only when highly relevant, with internal approval",
"source_url": "https://www.anthropic.com/news/an-update-on-independent-evaluators",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W42"
},
{
"id": "E1277",
"date": "2026-10-15",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "13",
"actor": "OpenAI",
"title": "OpenAI launches the Rosalind Academic Plan for academic research labs",
"event": "OpenAI launched the Rosalind Academic Plan for academic research labs — eligible labs get up to 30 Business Standard seats free for up to 12 months and can use GPT-Rosalind after trusted-access approval",
"significance": "It gives access to all commercially available frontier models within plan limits, continuing the competition among labs to offer free seats to university research labs",
"source_url": "https://openai.com/index/introducing-the-rosalind-academic-plan/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W42"
},
{
"id": "E1278",
"date": "2026-10-15",
"date_precision": "day",
"axis": "19",
"axis_name": "On-Device & Local Inference",
"also_axes": "18",
"actor": "Meta, Arm (PyTorch ExecuTorch)",
"title": "PyTorch ExecuTorch releases an Arm Cortex-M CPU backend",
"event": "PyTorch ExecuTorch released an Arm Cortex-M CPU backend — models run on microcontrollers without an Ethos-U NPU through the same PyTorch export and quantization flow",
"significance": "In the team's own measurements, 4 MLPerf Tiny models each ran in 21.5ms or less and under 90KiB of RAM on a single Cortex-M55 core of the Alif Ensemble E8 (the anomaly detection autoencoder in 5.2KiB). The team noted these are not an official MLPerf submission",
"source_url": "https://pytorch.org/blog/bringing-executorch-to-arm-cortex-m-cpus/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W42"
},
{
"id": "E1284",
"date": "2026-10-15",
"date_precision": "day",
"axis": "3",
"axis_name": "Tools & Agents",
"also_axes": "5",
"actor": "Hanzhang Zhou et al.",
"title": "OpenGCUA released",
"event": "OpenGCUA released — a study that leaves every component of a general-purpose computer-using agent open, combining screen control with code execution and moving the job of judging what to look at and how far the work has come from harness rules to the model",
"significance": "The authors reported that on OSWorld-v2 the average partial score of the 8 models evaluated rose by 19.6 points and that steps per task fell by 53.0% on average compared with running screen control alone. They released the implementation, prompts and evaluation logs together with an early version of a harness for real devices",
"source_url": "https://arxiv.org/abs/2610.22200",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W43"
},
{
"id": "E1285",
"date": "2026-10-15",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "9",
"actor": "Black Forest Labs",
"title": "Black Forest Labs releases FLUX.3 Image Klein open weights",
"event": "Black Forest Labs released the open weights of FLUX.3 Image Klein — one model handles making a picture from text, editing with several reference pictures, and layout control that places regions by writing coordinates into the prompt",
"significance": "It shipped two versions, a 16-step distilled one and a 50-step base one, together with FP8 and NVFP4 quantized weights and a DreamBooth LoRA training script. Only a speed figure was given — the distilled version makes a 1024×1024 picture in about 2 seconds on a single H100 — and no quality scores",
"source_url": "https://huggingface.co/blog/flux-3",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W43"
},
{
"id": "E1286",
"date": "2026-10-15",
"date_precision": "day",
"axis": "7",
"axis_name": "Multimodal",
"also_axes": "11",
"actor": "Luozheng Qin et al.",
"title": "MJev-Spatial released",
"event": "MJev-Spatial released — instead of writing out an answer to a spatial question as text, it solves it as a multiple-choice problem, scoring options, numbers and 2D coordinates with projections the model already has and settling the answer in a single computation",
"significance": "The authors reported that, trained on 12.3 million question-answer pairs on top of Qwen3.5-4B, it ranked No. 1 among the 8 models compared, with an overall 65.64%, and was No. 1 on 7 of 12 benchmarks. They reported it was 2.5 times faster than the fastest generative approach and 5.6 times faster than the model it was built on",
"source_url": "https://arxiv.org/abs/2610.22276",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W43"
},
{
"id": "E1287",
"date": "2026-10-15",
"date_precision": "day",
"axis": "8",
"axis_name": "Inference Infrastructure & Serving",
"also_axes": "9",
"actor": "IBM",
"title": "IBM releases Granite Switch 4.1",
"event": "IBM released Granite Switch 4.1 — to handle chat, judging and speech transcription from a single endpoint, it bundles the 8B and 30B models with a judging aLoRA, a Guardian aLoRA for safety judgments and Granite-Speech-5.0-Turbo-CTC",
"significance": "Because these are activated LoRAs, the KV cache built by the base model can be reused for the judgment that follows, and IBM said GPU memory with all three components added stays close to that of the base model alone. It gave almost no numerical comparisons, only a demonstration in which a customized 3B model judged 60 to 100 items per second",
"source_url": "https://huggingface.co/blog/ibm-granite/granite-switch-4-1",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W43"
},
{
"id": "E1288",
"date": "2026-10-15",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "11",
"actor": "Mistral AI",
"title": "Mistral AI releases Timestral, a time-series forecasting model",
"event": "Mistral AI released Timestral, a time-series forecasting model — four sizes, 5M, 28M, 123M and 209M, under a modified MIT license, producing probabilistic forecasts over 21 quantiles from a context of 32,768 time steps on CPU alone without a GPU",
"significance": "Company-reported, the 209M model ranks No. 1 in the pretrained zero-shot categories of GIFT-Eval, TIME and fev-bench, and the 28M model beat Chronos-2, which is about four times larger. On an 8-thread server CPU the 5M model takes about 10ms and the 209M model about 140ms. A technical report was only announced",
"source_url": "https://mistral.ai/news/timestral",
"source_type": "primary",
"claim_type": "statement",
"added_week": "2026-W43"
},
{
"id": "E1289",
"date": "2026-10-15",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "11",
"actor": "Bingxi Hou et al.",
"title": "Waypoint-OPD released",
"event": "Waypoint-OPD released — rather than taking the teacher model's change in probabilities before and after reinforcement learning as it stands, it compares them with a midpoint of the same training run and, where the direction of change before and after disagrees, damps that signal as it transfers it to the student model",
"significance": "It starts from the point that looking only at the endpoints lets preference changes from early and late training cancel each other out and hide the parts where the teacher changed consistently. The authors reported that with a Qwen3-4B teacher and three Qwen3-family students, in all six settings compared across the two domains of math and code, it had the highest domain average among the methods compared",
"source_url": "https://arxiv.org/abs/2610.21601",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W43"
},
{
"id": "E1290",
"date": "2026-10-15",
"date_precision": "day",
"axis": "13",
"axis_name": "Compute Economics & Geopolitics",
"also_axes": "10",
"actor": "Cohere, AISLE",
"title": "Cohere announces plans to acquire security company AISLE",
"event": "Cohere announced plans to acquire the security company AISLE — it agreed to buy the Czech company, founded in 2024, which builds systems that find and fix unknown vulnerabilities on their own, and set up a new Cohere Cyber organization",
"significance": "AISLE said that in its first year out of stealth it was assigned more than 500 CVEs across more than 100 codebases, and that it found all 12 vulnerabilities in OpenSSL's January security release and 18 of the 24 in curl's latest release. The amount was not disclosed, and Cohere said it will make Prague a base for AI security",
"source_url": "https://cohere.com/blog/cohere-and-aisle-sign-letter-of-intent-to-advance-cyber-resilient-sovereign-ai",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W43"
},
{
"id": "E1291",
"date": "2026-10-15",
"date_precision": "day",
"axis": "14",
"axis_name": "Physical World & Real-World Impact",
"also_axes": "",
"actor": "OpenAI",
"title": "OpenAI releases a Brazilian Portuguese course for teachers",
"event": "OpenAI released a Brazilian Portuguese course for teachers — on the free learning platform OpenAI Academy it opened an AI for Educators course for primary, middle and high school teachers, covering how to use AI for lesson design, activities, assessment and communication",
"significance": "It put protecting teachers' educational judgment forward as a premise, and, timed to Teachers' Day, also explained how to use ChatGPT's study mode (Study) in class. No figures on the number of users or on effectiveness were given",
"source_url": "https://openai.com/index/teaching-students-to-think-and-learn-through-questions/",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W43"
},
{
"id": "E1292",
"date": "2026-10-15",
"date_precision": "day",
"axis": "16",
"axis_name": "Evaluation & Benchmarks",
"also_axes": "3",
"actor": "Dongxu Yang et al.",
"title": "FDE-Gym released",
"event": "FDE-Gym released — a benchmark modeled on the work of an engineer posted to a customer, with 95 delivery tasks drawn from 20 situations across 18 industries that have to be solved while asking after vague requirements and constraints nobody stated",
"significance": "The authors reported that in the setting where the constraints have to be found by the model, even the best configuration averaged only 0.56 points and solved just 14 of the 95 tasks. Even in the 23 tasks where every necessary fact was obtained from the stakeholder, only 10 scored 0.8 or above, and they concluded that finding the unstated constraints is the bottleneck",
"source_url": "https://arxiv.org/abs/2610.22350",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W43"
},
{
"id": "E1293",
"date": "2026-10-16",
"date_precision": "day",
"axis": "9",
"axis_name": "Open-Weight Lineage",
"also_axes": "8",
"actor": "Reflection",
"title": "Reflection launches Beam, an open-weight model",
"event": "Reflection launched Beam, an open-weight model — a sparse mixture-of-experts architecture with 501 billion total and 23 billion active parameters per token, released under Apache 2.0 with a context of 1 million tokens",
"significance": "It is the first model from a new US lab. Company-reported scores were SWEBench Verified 81.6%, GPQA Diamond 90.6%, AIME 2026 97.7% and Terminal Bench v2.1 80.1% (on BF16), and it ships in three versions, BF16, FP8 and NVFP4, with the NVFP4 checkpoint at about 280GB",
"source_url": "https://huggingface.co/blog/reflection-beam",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W43"
},
{
"id": "E1294",
"date": "2026-10-16",
"date_precision": "day",
"axis": "12",
"axis_name": "Training Data & Post-Training",
"also_axes": "9",
"actor": "NVIDIA, AI4Bharat (IIT Madras)",
"title": "NVIDIA previews a 2.8 trillion token corpus for 22 Indian languages",
"event": "NVIDIA announced the coming release of a 2.8 trillion token pretraining corpus for 22 Indian languages — Nemotron-pretraining-Indic-v1, built with AI4Bharat at IIT Madras, comes together with an open generation pipeline that combines translation, quality scoring and restoration",
"significance": "Company-reported, it covers 34 language and script combinations with 2.601 trillion tokens in native scripts plus 224.6 billion in Latin script, and includes 438.4 billion restored tokens kept only where the quality score was 75 or above. Continuing the training of Nemotron 3.5 Super VL on part of this corpus raised five Indic benchmarks by 4.98 to 9.82 percentage points, with the caveat that this is not the effect of the synthetic corpus alone",
"source_url": "https://huggingface.co/blog/nvidia/scaling-indic-pretraining",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W43"
},
{
"id": "E1295",
"date": "2026-10-16",
"date_precision": "day",
"axis": "19",
"axis_name": "On-Device & Local Inference",
"also_axes": "13",
"actor": "NVIDIA",
"title": "NVIDIA launches RTX Spark laptops",
"event": "NVIDIA launched RTX Spark laptops — Windows laptops that pair Blackwell RTX graphics with its own AI platform and run models on the device with up to 128GB of unified memory, selling from $2,399",
"significance": "ASUS, Dell, HP, Lenovo, Microsoft and others are taking part in a staggered rollout. The launch put AI run on the device front and center alongside creative work and gaming, but compute performance (TOPS) was not stated in the press release",
"source_url": "https://nvidianews.nvidia.com/news/nvidia-rtx-spark-pcs-available-today",
"source_type": "primary",
"claim_type": "fact",
"added_week": "2026-W43"
}
]
}