There's a lot to like here! - Very smart way to use a masked autoencoder (unsupervised technique!) to build a world model from visual data. This makes other visually based world models I've seen seem clumsy in comparison. 1/4

Beautiful use of the BabyView dataset to train a visual learning model!

I think we finally made really significant progress on the biggest unsolved "developmental AI" problem: learning from human-scale data. Key idea: zero-shot world models that support concept extraction via approximate causal inference. amazing collab w/ @mcxfrank.bsky.social @khaiaw.bsky.social

So excited about this work using our data of children’s first-person experiences to train efficient, flexible visual learning models!

Children exhibit visual understanding from limited experience, orders of magnitude less than our best models. We introduce the Zero-shot World Model (ZWM). Trained on a single child's visual experience, BabyZWM rapidly generates competence across diverse benchmarks with no task-specific training. 🧵

Children exhibit visual understanding from limited experience, orders of magnitude less than our best models. We introduce the Zero-shot World Model (ZWM). Trained on a single child's visual experience, BabyZWM rapidly generates competence across diverse benchmarks with no task-specific training. 🧵

LLMs can retrieve knowledge — but can they connect it in *creative* ways to solve problems? Introducing CresOWLve 🦉, a new benchmark that evaluates creative problem-solving over real-world knowledge, using puzzles that require multiple creative thinking strategies.👇

Here is our best thinking about how to make world models. I would apologize for it being a massive 40-page behemoth, but it's worth reading. arxiv.org/pdf/2509.09737

Happy to share that our BBS target article has been accepted: “Core Perception”: Re-imagining Precocious Reasoning as Sophisticated Perceiving With Alon Hafri, @veroniqueizard.bsky.social, @chazfirestone.bsky.social & Brent Strickland Read it here: doi.org/10.1017/S014... A short thread [1/5]👇