A new diagnostic protocol has been developed to test the physics reasoning capabilities of frontier large language models (LLMs) in unfamiliar conceptual frameworks. This auditable four-stage process, which includes locked pre-registrations and dual-LLM judging, was applied to three distinct parallel physical worlds: a single-equation world ($F=mv$), Aristotelian mechanics, and a more complex 'Decay World'. The study found that while models like Claude Opus 4.7, GPT-5.5, and Gemini 3.1 Pro showed some success in content and structural reasoning in simpler worlds, they struggled significantly in the Decay World, often computing incorrect ratios due to a tendency to revert to standard physics principles. The research also highlighted limitations in LLM-judge reliability across different frameworks and a weakness in self-review capabilities. AI
IMPACT Highlights limitations in LLM reasoning beyond familiar patterns, suggesting a need for more robust evaluation methods for complex problem-solving.
RANK_REASON Academic paper detailing a new methodology for evaluating LLM capabilities. [lever_c_demoted from research: ic=1 ai=1.0]
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