MARDoc: A Memory-Aware Refinement Agent Framework for Multimodal Long Document QA

• URL: http://arxiv.org/abs/2606.05749v1
• Date: Thu, 04 Jun 2026 06:23:01 GMT
• Title: MARDoc: A Memory-Aware Refinement Agent Framework for Multimodal Long Document QA
• Abstract summary: We propose a Memory-Aware Refinement Agent framework that decouples long-document QA into three specialized agents.<n>Across iterations, the agents rely on a dynamically updated structured memory rather than a full accumulated interaction history.<n>Experiments on MMLongBench-Doc and DocBench show that MARDoc achieves strong results, outperforming same-backbone baselines.
• Score: 23.25080415779712
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Iterative retrieval-reasoning agents have recently shown promise for multimodal long-document question answering. However, most existing systems maintain a single growing context that mixes retrieval traces, observations, and intermediate reasoning. As interactions accumulate, key evidence becomes scattered and diluted, making multi-hop reasoning noisy. We propose MARDoc, a Memory-Aware Refinement Agent framework that decouples long-document QA into three specialized agents: an Explorer for multi-granularity multimodal retrieval, a Refiner for distilling interaction traces into structured evidence and reasoning memories, and a Reflector for checking evidence sufficiency and providing targeted feedback. Across iterations, the agents rely on a dynamically updated structured memory rather than a full accumulated interaction history. This design reduces context noise while preserving answer-critical facts and their logical dependencies. Experiments on MMLongBench-Doc and DocBench show that MARDoc achieves strong results, outperforming same-backbone baselines and demonstrating the effectiveness of structured memory for agentic document QA.

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