---
格式版本: 2
标题: "InSight-doc: Agentic Visual Perception for Long-Document Understanding"
原文链接: "https://arxiv.org/abs/2608.10628"
发布日期: "2026-08-11"
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入库时间: "2026-08-12T03:23:36.144Z"
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去重键: "https://arxiv.org/abs/2608.10628"
---

## Computer Science > Computer Vision and Pattern Recognition

## Title:InSight-doc: Agentic Visual Perception for Long-Document Understanding

Authors:[Kaican Li](https://arxiv.org/search/cs?searchtype=author&query=Li,+K), [Weiyan Xie](https://arxiv.org/search/cs?searchtype=author&query=Xie,+W), [Lewei Yao](https://arxiv.org/search/cs?searchtype=author&query=Yao,+L), [Jiannan Wu](https://arxiv.org/search/cs?searchtype=author&query=Wu,+J), [Lanqing Hong](https://arxiv.org/search/cs?searchtype=author&query=Hong,+L), [Yongxiang Huang](https://arxiv.org/search/cs?searchtype=author&query=Huang,+Y), [Nevin L. Zhang](https://arxiv.org/search/cs?searchtype=author&query=Zhang,+N+L)

[View PDF](https://arxiv.org/pdf/2608.10628) [HTML (experimental)](https://arxiv.org/html/2608.10628v1)

> Abstract:Long-document understanding often requires reasoning over many visually rich pages, making inference costly and prone to context rot. In this work, we propose InSight-doc, an agentic visual perception framework that treats visual resolution as an adaptive reasoning-time resource. InSight-doc starts from low resolution and selectively zooms into high-resolution regions for finer evidence, without relying on any external retriever. To train such an agent, we construct an active-perception corpus of 17.9K high-quality SFT examples with region-level zoom-in trajectories, accompanied by 19.2K hard RL examples. Through SFT+RL, InSight-doc-8B improves the baseline by 4.3--16.4 accuracy points over document VQA benchmarks. On long documents, it reduces hallucination by more than 40% and inference latency by 41%--68% while maintaining an accuracy lead. Our code, datasets, and model are released at [this https URL](https://github.com/m-Just/InSight-doc).

| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL); Machine Learning (cs.LG) |
| --- | --- |
| Cite as: | [arXiv:2608.10628](https://arxiv.org/abs/2608.10628) \[cs.CV\] |
|  | (or [arXiv:2608.10628v1](https://arxiv.org/abs/2608.10628v1) \[cs.CV\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.10628](https://doi.org/10.48550/arXiv.2608.10628) |

## Submission history

From: Kaican Li \[[view email](https://arxiv.org/show-email/4d923260/2608.10628)\]  
**\[v1\]** Tue, 11 Aug 2026 08:15:16 UTC (5,373 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.10628) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
