CogDoc: Towards Unified thinking in Documents

• URL: http://arxiv.org/abs/2512.12658v1
• Date: Sun, 14 Dec 2025 12:14:17 GMT
• Title: CogDoc: Towards Unified thinking in Documents
• Authors: Qixin Xu, Haozhe Wang, Che Liu, Fangzhen Lin, Wenhu Chen,
• Abstract summary: We propose 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.<n>We conduct a rigorous investigation into post-training strategies for the unified thinking framework, demonstrating that a Direct Reinforcement Learning approach outperforms RL with Supervised Fine-Tuning (SFT)<n>Specifically, we find that direct RL avoids the "policy conflict" observed in SFT.
• Score: 53.41571589733423
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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