Lost in Perception: Isolating Perceptual and Reasoning Failures in Multimodal Physics and Geometry Reasoning

Abstract: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.

Artificial Intelligence (cs.AI)

arXiv:2609.18991 [cs.AI]

(or arXiv:2609.18991v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.18991 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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