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arXiv · 2510.20696

Diagnosing Visual Reasoning: Challenges, Insights, and a Path Forward

Jing Bi·Guangyu Sun·Ali Vosoughi·Chen Chen·Chenliang Xu
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Abstract

Multimodal large language models (MLLMs) that integrate visual and textual reasoning leverage chain-of-thought (CoT) prompting to tackle complex visual tasks, yet continue to exhibit visual hallucinations and an over-reliance on textual priors. We present a systematic diagnosis of state-of-the-art vision-language models using a three-stage evaluation framework, uncovering key failure modes. To address these, we propose an agent-based architecture that combines LLM reasoning with lightweight visual modules, enabling fine-grained analysis and iterative refinement of reasoning chains. Our results highlight future visual reasoning models should focus on integrating a broader set of specialized tools for analyzing visual content. Our system achieves significant gains (+10.3 on MMMU, +6.0 on MathVista over a 7B baseline), matching or surpassing much larger models. We will release our framework and evaluation suite to facilitate future research.

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Jing Bi, Guangyu Sun, Ali Vosoughi, Chen Chen, Chenliang Xu. 2025-10-23. Diagnosing Visual Reasoning: Challenges, Insights, and a Path Forward. https://arxiv.org/abs/2510.20696

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