CMU researchers publish a study on world models learning object permanence
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
Why it matters
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 — Primary sources first; secondary reports are labelled “Press”
Officialhuggingface.cohttps://huggingface.co/papers/2609.28654Added in Week 40, 2026 · See that week