Author

D. Yamins

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#artificial intelligence Preprint Oct 2026

$\Psi$-0.6: Adaptive Streams of Thought in a Unified Vision-Language World Model

Humans reason about the physical world by flexibly combining different forms of inference as a problem unfolds. Achieving this flexibility is a major open challenge in AI: capabilities often remain separated across specialized models, limiting how they can be composed and jointly adapted to a task. We introduce $\Psi$-...

Khai Loong Aw, Klemen Kotar, Bai-Han Zhang et al. · 0 citations

#artificial intelligence Apr 2026

Zero-shot World Models Are Developmentally Efficient Learners

It is shown that ZWM can be learned from the first-person experience of a single child, rapidly generating competence across multiple physical understanding benchmarks, and shows progressive, staged emergence of capacities during learning and builds brain-like internal representations.

Khai Loong Aw, Klemen Kotar, Wanhee Lee et al. · arXiv.org · 6 citations