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MARDoc framework enhances multimodal long document QA with structured memory

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced MARDoc, a novel framework designed to improve question answering for long, multimodal documents. This system utilizes three specialized agents: an Explorer for retrieval, a Refiner for processing interactions into structured memories, and a Reflector for feedback. By employing a dynamic structured memory instead of a continuously growing context, MARDoc aims to reduce noise and preserve critical information for more effective multi-hop reasoning. AI

IMPACT Introduces a new method for handling complex, multimodal documents, potentially improving AI's ability to process and reason over extensive information.

RANK_REASON The cluster describes a new research paper detailing a novel framework for a specific AI task.

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AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Kaifeng Chen, Hongtao Liu, Qiyao Peng, Jian Yang, Yongqiang Liu, Xiaochen Zhang, Qing Yang ·

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

    arXiv:2606.05749v1 Announce Type: new 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 interm…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

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

    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, ke…

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