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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.11025 |
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| _version_ | 1866914467787309056 |
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| author | Jiang, Zheng Chen, Yiming He, Nan Chen, Jiahui Li, Chaoyang Qian, Houde Sun, Lifeng |
| author_facet | Jiang, Zheng Chen, Yiming He, Nan Chen, Jiahui Li, Chaoyang Qian, Houde Sun, Lifeng |
| contents | Recent multimodal large language models (MLLMs) have begun to support Thinking with Images by invoking visual tools such as zooming and cropping during inference. Yet these systems remain brittle in fine-grained visual reasoning because they must decide where to look before they have access to the evidence needed to make that decision correctly. We identify this circular dependency as the Grounding Paradox. To address it, we propose Test-Time Scaling over Perception (TTSP), a framework that treats perception itself as a scalable inference process. TTSP generates multiple exploratory perception traces, filters unreliable traces using entropy-based confidence estimation, distills validated observations into structured knowledge, and iteratively refines subsequent exploration toward unresolved uncertainty. Extensive experiments on high-resolution and general multimodal reasoning benchmarks show that TTSP consistently outperforms strong baselines across backbone sizes, while also exhibiting favorable scalability and token efficiency. Our results suggest that scaling perception at test time is a promising direction for robust multimodal reasoning under perceptual uncertainty. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_11025 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Test-time Scaling over Perception: Resolving the Grounding Paradox in Thinking with Images Jiang, Zheng Chen, Yiming He, Nan Chen, Jiahui Li, Chaoyang Qian, Houde Sun, Lifeng Computer Vision and Pattern Recognition Recent multimodal large language models (MLLMs) have begun to support Thinking with Images by invoking visual tools such as zooming and cropping during inference. Yet these systems remain brittle in fine-grained visual reasoning because they must decide where to look before they have access to the evidence needed to make that decision correctly. We identify this circular dependency as the Grounding Paradox. To address it, we propose Test-Time Scaling over Perception (TTSP), a framework that treats perception itself as a scalable inference process. TTSP generates multiple exploratory perception traces, filters unreliable traces using entropy-based confidence estimation, distills validated observations into structured knowledge, and iteratively refines subsequent exploration toward unresolved uncertainty. Extensive experiments on high-resolution and general multimodal reasoning benchmarks show that TTSP consistently outperforms strong baselines across backbone sizes, while also exhibiting favorable scalability and token efficiency. Our results suggest that scaling perception at test time is a promising direction for robust multimodal reasoning under perceptual uncertainty. |
| title | Test-time Scaling over Perception: Resolving the Grounding Paradox in Thinking with Images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.11025 |