Axiomatic World Modeling for Physics Reasoning

Key points

• Recent research-level physics-reasoning benchmarks for LLMs reveal a failure mode that lies not in knowledge or execution, but in modeling the world being reasoned about.1
• We define this premise-level divergence as modeling drift: the gap between a model's internal axiomatic world model (AWM) and the true physical world specified in the problem.1
• RLAWM probes physics reasoning at two hierarchical levels (modeling and solving), each scored by a physically grounded reward.1
• The solving reward targets instantiated knowledge reasoning, checking whether the model's concrete derivation remains consistent with the axiomatic world.1

Sources (2)

• [1]Axiomatic World Modeling for Physics Reasoning
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 17, 07:29 AM

  We propose Reinforcement Learning with Axiomatic World Modeling (RLAWM), which optimizes the policy by penalizing divergence between its inferred axiomatic world model and the true physics world.

  Recent research-level physics-reasoning benchmarks for LLMs reveal a failure mode that lies not in knowledge or execution, but in modeling the world being reasoned about.

• [2]Axiomatic World Modeling for Physics Reasoning
  Hugging Face Daily Papers · Oct 17, 12:00 AM · same content

Extractive summary: sentences quoted from the sources.