MARDoc: A Memory-Aware Refinement Agent Framework for Multimodal Long Document QA
Researchers introduce MARDoc, a Memory-Aware Refinement Agent framework that improves multimodal long-document question answering by decoupling the task into three specialized agents (Explorer, Refiner, Reflector) that maintain structured memory instead of accumulated interaction history. The approach reduces context noise while preserving critical evidence, outperforming baseline systems on benchmark datasets.
MARDoc addresses a fundamental challenge in AI systems handling complex document analysis: as iterative reasoning processes accumulate information, the signal-to-noise ratio degrades because retrieval traces, observations, and intermediate reasoning become intermingled in a single growing context. This problem becomes acute in multimodal scenarios where documents contain both text and visual elements across multiple pages or sections. The researchers' solution introduces architectural specialization—delegating retrieval, evidence refinement, and validation to distinct agents—enabling each component to focus on its core function without distraction from extraneous interaction history.
The structured memory approach reflects a broader trend in AI development toward mimicking human cognitive processes. Rather than maintaining monolithic conversation logs, the framework distills interactions into organized, retrievable evidence and reasoning maps. This design principle aligns with advances in prompt engineering and agentic systems where intermediate reasoning artifacts are treated as first-class objects.
For the AI development community, MARDoc's results on MMLongBench-Doc and DocBench demonstrate measurable improvements in document QA systems, which have practical applications across legal analysis, research synthesis, and enterprise knowledge management. The work validates that structured memory management yields better performance than naive context accumulation, a principle likely to influence future agent architecture designs.
The framework's effectiveness suggests demand for more sophisticated document processing capabilities. Teams building document-intensive applications—from contract review to technical documentation analysis—stand to benefit from these advances. Future developments may integrate similar memory structures into broader AI agent ecosystems, potentially enabling more reliable long-context reasoning across diverse tasks.
- →MARDoc decouples document QA into three specialized agents (Explorer, Refiner, Reflector) to reduce context noise and improve reasoning
- →Structured, dynamically updated memory outperforms accumulated interaction histories in preserving answer-critical evidence
- →The framework demonstrates measurable improvements on MMLongBench-Doc and DocBench benchmarks over baseline systems
- →Agent-based architectures with specialized roles enhance multimodal document processing at scale
- →Memory management principles in this framework could influence broader AI agent design patterns