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arXiv AI By Raj Jaiswal, Sree Krishna Uppalapati, Dhruvkumar Patel, Ria Khatoniar, Tanuja Ganu, Rajiv Ratn Shah

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

Read the original on arXiv AI →

The paper introduces a five-task diagnostic experiment that separates perceptual and reasoning failures in multimodal large language models on physics and geometry benchmarks. It finds that misinterpreting diagrams hurts performance even on text-only solvable problems, and that accuracy improves when models receive human-authored captions. The study shows that correcting captions can recover many errors, revealing distinct reasoning bottlenecks that differ by domain, while a heavily pretrained model still underperforms and often truncates reasoning traces.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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