Algebraic World Model enables AI to derive fundamental laws from observations

The Algebraic World Model (AWM) from Tsinghua University's Yao Qizhi team teaches AI to derive fundamental rules from observations, moving beyond simple pattern recognition. AWM abstracts game objects and rules into an algebraic structure, then uses three steps -- exploration, refinement, and proof in Lean 4 language -- to formalize these into mathematical assertions. In tests, AWM achieved 99.9% on the ARC-AGI-3 benchmark, solving 183 levels with 43% of the actions needed by human baseline, and crucially demonstrated the ability to correctly identify unsolvable tasks and provide accurate step-count estimates.