InSight-doc: Agentic Visual Perception for Long-Document Understanding
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Computer Science > Computer Vision and Pattern Recognition
Title:InSight-doc: Agentic Visual Perception for Long-Document Understanding
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 .
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2608.10628 [cs.CV] |
| (or arXiv:2608.10628v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2608.10628
arXiv-issued DOI via DataCite (pending registration)
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