Diagnosing Visual Reasoning: Challenges, Insights, and a Path Forward
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912666504658944 |
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| author | Bi, Jing Sun, Guangyu Vosoughi, Ali Chen, Chen Xu, Chenliang |
| author_facet | Bi, Jing Sun, Guangyu Vosoughi, Ali Chen, Chen Xu, Chenliang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_20696 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Diagnosing Visual Reasoning: Challenges, Insights, and a Path Forward Bi, Jing Sun, Guangyu Vosoughi, Ali Chen, Chen Xu, Chenliang Computer Vision and Pattern Recognition 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. |
| title | Diagnosing Visual Reasoning: Challenges, Insights, and a Path Forward |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.20696 |