Tsinghua Researchers Introduce Algebraic World Model to Learn Rules and Formalize Proofs

A Tsinghua University team led by Andrew Yao and Yang Yuan introduced Algebraic World Model (AWM), a method for learning rules in interactive worlds that, according to QbitAI, completed all 183 ARC-AGI-3 levels with a 99.9% score while using 43% of the human baseline’s average action count. AWM turns observations into executable Python hypotheses, refines abstract rules through exploration, and formalizes claims in Lean 4. QbitAI also reported that it proved an altered level impossible to complete within that level’s step budget. The proofs depend on whether exploration captures the relevant rules, so their applicability to the interactive world rests on the rules the system learns.

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