SCoPE VLM: Selective Context Processing for Efficient Document Navigation in Vision-Language Models

• URL: http://arxiv.org/abs/2510.21850v1
• Date: Wed, 22 Oct 2025 17:47:12 GMT
• Title: SCoPE VLM: Selective Context Processing for Efficient Document Navigation in Vision-Language Models
• Authors: Gyubeum Lim, Yemo Koo, Vijay Krishna Madisetti,
• Abstract summary: Understanding long-context visual information remains a fundamental challenge for vision-language models.<n>We propose SCoPE VLM, a document navigation expert that leverages a novel Chain of Scroll mechanism.<n>SCoPE VLM is the first framework to explicitly model agentic reading patterns in multi-page document question answering.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding long-context visual information remains a fundamental challenge for vision-language models, particularly in agentic tasks such as GUI control and web navigation. While web pages and GUI environments are inherently structured documents, current VLMs typically neglect decision-oriented document understanding in their training objectives. Existing approaches primarily extend visual embeddings to process long, high-resolution inputs, but these methods are memory-intensive and impractical for locally deployable solutions. To address these issues, we propose SCoPE VLM, a document navigation expert that leverages a novel Chain of Scroll mechanism to selectively and recursively navigate documents, focusing exclusively on relevant segments. We introduce a dedicated data generation pipeline to construct informative Chain of Scroll trajectories and Episodic Group Relative Policy Optimization, a tailored reinforcement learning method to reduce the gap between training and inference. Our method substantially reduces memory usage and effectively models human-like reading behaviors. To the best of our knowledge, SCoPE VLM is the first framework to explicitly model agentic reading patterns in multi-page document question answering, advancing the capabilities of multimodal agents.

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