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

2026-06-04 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

MARDoc, a Memory-Aware Refinement Agent framework, addresses challenges in multimodal long-document question answering where existing systems suffer from diluted evidence and noisy multi-hop reasoning due to a single, growing context. This framework decouples the QA process into three specialized agents: an Explorer for multi-granularity multimodal retrieval, a Refiner for distilling interaction traces into structured evidence, and a Reflector for checking evidence sufficiency. MARDoc relies on a dynamically updated structured memory, rather than a full interaction history, to reduce context noise while preserving answer-critical facts and their logical dependencies. Experiments on MMLongBench-Doc and DocBench demonstrate MARDoc's strong performance, outperforming same-backbone baselines and validating the effectiveness of its structured memory approach for agentic document QA.

Key takeaway

For AI Scientists and Machine Learning Engineers developing multimodal long-document QA systems, MARDoc's agentic framework offers a robust alternative to single-context approaches. You should consider adopting a decoupled agent architecture with dynamic structured memory management to enhance multi-hop reasoning and reduce context dilution. This design can significantly improve the accuracy and efficiency of your systems, especially when dealing with extensive and complex documents.

Key insights

MARDoc uses specialized agents and structured memory to refine multimodal long-document QA, reducing noise and preserving critical evidence.

Principles

• Decouple complex QA into specialized agent roles.
• Structured memory reduces context noise in iterative reasoning.
• Dynamically update memory for critical facts and dependencies.

Method

MARDoc employs an Explorer for multimodal retrieval, a Refiner for distilling traces into structured memories, and a Reflector for evidence sufficiency checks, all relying on dynamic structured memory.

In practice

• Implement specialized agents for distinct QA tasks.
• Design dynamic structured memory for iterative reasoning.
• Evaluate agentic QA on MMLongBench-Doc and DocBench.

Topics

• Multimodal QA
• Agent Frameworks
• Structured Memory
• Long Document QA
• Retrieval-Reasoning
• Context Management

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.