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arXiv · 2512.12658

CogDoc: Towards Unified thinking in Documents

Qixin Xu·Haozhe Wang·Che Liu·Fangzhen Lin·Wenhu Chen
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Abstract

Current document reasoning paradigms are constrained by a fundamental trade-off between scalability (processing long-context documents) and fidelity (capturing fine-grained, multimodal details). To bridge this gap, we propose CogDoc, a unified coarse-to-fine thinking framework that mimics human cognitive processes: a low-resolution "Fast Reading" phase for scalable information localization,followed by a high-resolution "Focused Thinking" phase for deep reasoning. We conduct a rigorous investigation into post-training strategies for the unified thinking framework, demonstrating that a Direct Reinforcement Learning (RL) approach outperforms RL with Supervised Fine-Tuning (SFT) initialization. Specifically, we find that direct RL avoids the "policy conflict" observed in SFT. Empirically, our 7B model achieves state-of-the-art performance within its parameter class, notably surpassing significantly larger proprietary models (e.g., GPT-4o) on challenging, visually rich document benchmarks.

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Qixin Xu, Haozhe Wang, Che Liu, Fangzhen Lin, Wenhu Chen. 2025-12-14. CogDoc: Towards Unified thinking in Documents. https://arxiv.org/abs/2512.12658

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