Lost in Perception: Isolating Perceptual and Reasoning Failures in Multimodal Physics and Geometry Reasoning
On the map: LLM reasoning → Multimodal reasoning
Abstract (arXiv)
Multimodal LLMs report strong performance on scientific reasoning benchmarks, yet most treat perception and reasoning as a single measurable process. We introduce a five-task diagnostic experiment across physics and geometry benchmarks that isolates failures to perception, reasoning, or both. Incorrect diagram interpretation degrades performance even on problems models solve correctly from text alone, and accuracy generally rises from raw images to human-authored captions. Recovery under corrected captions is high for some models, separating perception-blocked failures from genuine reasoning bottlenecks. Which reasoning error follows a perception failure depends on domain: physics failures resolve into calculation errors, geometry into conceptual misapplication. As a discussion beyond our core experiments, InternS1-mini, despite heavy scientific pretraining and thinking capabilities, falls below the weakest model from experiments on every task, with reasoning traces frequently truncating before completion.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Multimodal reasoning, one of 17 directions in LLM reasoning. Builds on 23 of its 29 references that are in the map. First posted on arXiv 17 Jul 2026.
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23 of 29 references.
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Same direction, newest first
- 2025Diagnosing Visual Reasoning: Challenges, Insights, and a Path Forward23 Oct
- 2023MMBench: Is Your Multi-modal Model an All-around Player?12 Jul
Other references · 21
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- 2024OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problemscited by 1,136
- 2023Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Promptingcited by 1,122
- 2023Measuring Faithfulness in Chain-of-Thought Reasoningcited by 703
- 2022Towards Reasoning in Large Language Models: A Surveycited by 596
- 2024MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?cited by 586
9 more
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- 2025PhysReason: A Comprehensive Benchmark towards Physics-Based Reasoningcited by 59
- 2024A Survey of Mathematical Reasoning in the Era of Multimodal Large Language Model: Benchmark, Method & Challengescited by 55
- 2025Intern-S1: A Scientific Multimodal Foundation Modelcited by 53
- 2025SeePhys: Does Seeing Help Thinking? -- Benchmarking Vision-Based Physics Reasoningcited by 34
- 2024Improving Physics Reasoning in Large Language Models Using Mixture of Refinement Agentscited by 24
- 2025PhysUniBench: A Multi-Modal Physics Reasoning Benchmark at Undergraduate Levelcited by 16
- 2025Seeing is Fixing: Cross-Modal Reasoning with Multimodal LLMs for Visual Software Issue Fixingcited by 12
- 2025Beyond Correctness: Exposing LLM-generated Logical Flaws in Reasoning via Multi-step Automated Theorem Provingcited by 6
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