Physics reasoning fails when LLMs silently corrupt problem premises
Source published ·Modelwire updated
Original coverage: arXiv cs.CL ↗·How Modelwire adds context
The development
A new research direction targets a blind spot in LLM reasoning: models systematically corrupt problem premises by silently altering quantities, inventing physics laws, or smuggling unstated assumptions to fit familiar patterns. This 'modeling drift' between a model's internal world model and the actual problem specification explains failures on physics benchmarks that existing reinforcement learning approaches like RLVR and PRM don't address. The proposed RLAWM framework directly supervises axiomatic world modeling, suggesting that reasoning failures often stem not from logical execution but from foundational misalignment with problem constraints. This reframes how the field should think about scaling reasoning capabilities.
Modelwire’s AI-generated summary of coverage from arXiv cs.CL.
Modelwire analysis
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The paper isolates a failure mode orthogonal to reasoning execution: LLMs don't just make logical mistakes, they silently rewrite problem premises before reasoning begins. This is not about inference chains or memory retention, but about whether the model's internal problem setup matches the actual specification.
Recent coverage has mapped reasoning failures into specific stages. 'Execution Before Memory' showed that video models fail at performing instructed changes, not remembering them. 'One-Pixel of Reasoning' demonstrated that a single corrupted logical warrant derails inference chains. RLAWM adds a layer before both: the model may never receive the correct premises to reason from. The MisleadQA work on subtly misaligned true information is closest in spirit, but that targets retrieval context, whereas this targets the model's own internal representation of the problem itself.
If RLAWM-trained models outperform RLVR and PRM on physics benchmarks specifically designed to catch premise corruption (e.g., problems where a single unstated assumption flip changes the answer), while showing no advantage on standard reasoning tasks, that confirms the diagnosis. If gains are uniform across benchmark types, the framework may be solving something broader than claimed.
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Coverage behind this analysis
These archive entries ground the connection in our analysis. They are ordered by source publication date, with links to our coverage and the original sources.
·arXiv cs.CL
Video world models fail at execution, not memory, new evaluation reveals
Researchers have identified a critical diagnostic gap in how video world models are evaluated. Existing benchmarks conflate three distinct tasks into a single score, masking whether failures stem from scene setup, instruction execution, or memory retention. The new MIST evaluation framework decouples these stages, revealing that models often fail not at remembering but at actually…
MentionsRLAWM · RLVR · PRM · LLMs
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Modelwire summarizes, we don’t republish. arXiv cs.CL originally reported this story as “Axiomatic World Modeling for Physics Reasoning”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.