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 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 di
The rapid advancement in multimodal AI and the increasing complexity of AI agent architectures are driving the need for more sophisticated memory management in long-document QA systems.
This development addresses a critical limitation in current AI agents, enabling more effective multi-hop reasoning over vast, complex information and improving their autonomous capabilities.
AI agents will become more adept at processing and reasoning over very large and diverse datasets, reducing information dilution and enhancing accuracy in complex tasks like research and analysis.
- · AI Agent Developers
- · Enterprise AI Solutions
- · Researchers
- · Knowledge Management Platforms
- · Legacy Search Engines (for complex queries)
- · Human Manual Information Synthesizers (for large datasets)
Improved performance and reliability of multimodal AI agents in handling extensive documents and complex queries.
Increased adoption of AI agents for advanced analytical tasks currently requiring significant human expert intervention.
The emergence of new business models built around hyper-efficient, long-document understanding and synthesis by AI.
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Read at arXiv cs.CL