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⑨27 records
Open-Weight Lineage
- Oct 2, 2026Allen Institute for AI (Ai2) — 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. SourceThe 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.
- Dec 1, 2025DeepSeek — DeepSeek-V3.2·V3.2-Speciale 공개 — V3.2-Exp의 정식 후속, 툴 사용 중 사고(Thinking in Tool-Use) 지원, 두 모델 가중치 MIT 공개(Speciale는 API에서 12월 15일까지 임시 제공)KO Source자사 주장 기준 V3.2-Speciale가 IMO·IOI 2025 등에서 금메달 수준 결과를 냈다고 밝힌 오픈웨이트 추론 모델
- Nov 6, 2025Moonshot AI — Kimi K2 Thinking 공개 — 총 1T·활성 32B MoE, 256K 컨텍스트, 사후학습 단계 QAT로 네이티브 INT4 추론, Modified MITKO Source사고 과정과 도구 호출을 번갈아 수행하도록 학습한 오픈웨이트 추론 에이전트로, 자사 주장 기준 사람 개입 없이 200~300회 연속 도구 호출을 유지
- Aug 21, 2025DeepSeek — DeepSeek-V3.1 공개 — 한 모델에서 사고(Think)·비사고(Non-Think) 모드 전환, 총 671B·활성 37B, 128K 컨텍스트, V3 위에 장문맥 확장용 840B 토큰 추가 학습, MITKO SourceDeepSeek가 에이전트 시대로 가는 첫걸음이라고 밝힌 릴리스로, API의 deepseek-chat·deepseek-reasoner가 한 모델의 비사고·사고 모드가 되면서 V3와 R1으로 나뉘어 있던 제공 방식이 합쳐짐
⑥17 records
Reasoning Models & the AGI Debate
- Sep 29, 2026OpenAI — 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. SourceA 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.
- Sep 28, 2026Anthropic — 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. SourceThe 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.
- Feb 17, 2026Anthropic — Claude Sonnet 4.6 출시 — 1M 토큰 컨텍스트(베타), 100만 토큰당 $3/$15 유지KO SourceFree·Pro 요금제의 claude.ai와 Claude Cowork 기본 모델이 됐고, 조기 접근 개발자들이 전작보다 크게 선호했으며 2025년 11월의 Opus 4.5보다도 자주 선호했다고 자사 발표
- Dec 11, 2025OpenAI — GPT-5.2 출시 — Instant·Thinking·Pro 3종, 전문 지식 업무·장시간 에이전트용 프런티어 모델 표방KO Source자사 측정 GPT-5.2 Thinking이 GDPval 승·무 70.9%(GPT-5 38.8%), ARC-AGI-2(Verified) 52.9%(GPT-5.1 Thinking 17.6%), SWE-bench Verified 80.0%
①4 records
Prompt Engineering
- Aug 9, 2023Anthropic — Claude Instant 1.2 API 출시 — 더 빠르고 저렴한 경량 모델에 Claude 2의 강점 반영KO Source자사 측정으로 Codex 58.7%(Instant 1.1 52.8%), GSM8K 86.7%(80.9%)이며 환각이 줄고 탈옥 저항이 높아졌다고 밝힘
- Jul 11, 2023Anthropic — Claude 2 출시 — 프롬프트당 최대 100K 토큰 입력, API와 함께 공개 베타 웹사이트 claude.ai 개시(미국·영국)KO Source자사 측정으로 Codex HumanEval 71.2%(Claude 1.3 56.0%), GSM8k 88.0%(85.2%), 미국 변호사 시험 객관식 76.5%(73.0%)이며 API 가격은 Claude 1.3과 동일
- May 10, 2023Google — PaLM 2 발표 — 100개 이상 언어로 다국어 학습 강화, Gecko·Otter·Bison·Unicorn 4개 크기KO SourceGoogle I/O에서 Bard를 포함한 25개 이상 제품·기능에 적용한다고 발표했으며, 가장 작은 Gecko는 오프라인 모바일 기기에서도 구동된다고 밝힘
- Feb 6, 2023Google — Bard 발표 — LaMDA 기반 실험적 대화형 AI 서비스, 신뢰 테스터에게 먼저 공개KO SourceLaMDA 경량 버전으로 시작해 연산 부담을 줄이고 외부 피드백을 모은 뒤 수 주 안에 일반 공개하겠다고 밝힘
③4 records
Tools & Agents
- Sep 29, 2026OpenClaw Foundation, Red Hat, NVIDIA — 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. SourceA 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.
- Sep 29, 2026OpenAI — 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. SourceThe 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.
- Sep 28, 2026SpaceXAI (xAI) — 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. SourceA 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.
- Sep 24, 2026Microsoft — 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. SourceThe 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.
⑩4 records
Safety, Alignment & Interpretability
- Sep 30, 2026OpenAI — 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. SourceThe 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.
- Sep 29, 2026Anthropic — 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. SourceThe 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.
- Sep 28, 2026OpenAI — 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". SourceA 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".
- Sep 24, 2026Microsoft — 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. SourceA 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".
⑪4 records
Model Architecture & Training Methods
- Oct 1, 2026Hui Ren et al. (UIUC) — 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. SourceThe 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".
- Apr 4, 2022Google Research — PaLM(Pathways Language Model) 발표 — 540B 밀집 디코더 전용 Transformer, TPU v4 칩 6,144개에서 Pathways로 학습KO SourcePathways 시스템을 대규모 학습에 처음 쓴 사례로, 영어 NLP 과제 29개 중 28개에서 GPT-3·Gopher·Chinchilla 등 기존 대형 모델의 퓨샷 성능을 넘었다고 보고
- Oct 11, 2018Google (Devlin 외) — BERT 논문 arXiv 공개 — 좌우 문맥을 함께 보는 양방향 Transformer 인코더 사전학습KO Source출력층 하나만 더해 미세조정하는 방식으로 11개 자연어 처리 과제에서 최고 성능을 기록했으며 GLUE 80.4%·SQuAD v1.1 F1 93.2(저자 보고)
- Jun 11, 2018OpenAI (Radford 외) — GPT(통칭 GPT-1) 논문 Improving Language Understanding by Generative Pre-Training 공개 — 12층 디코더 전용 Transformer를 BooksCorpus로 사전학습한 뒤 과제별 미세조정KO Source비지도 생성 사전학습과 지도 미세조정을 결합해 연구한 12개 과제 중 9개에서 당시 최고 성능을 경신했다고 보고
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