The research paper introduces the Memory Intelligence Agent (MIA), a sophisticated framework designed to improve how AI handles complex, multi-step web research. Unlike traditional systems that struggle with data overload, MIA utilizes a Manager-Planner-Executor architecture to organize information into structured, process-oriented memories. This approach allows the agent to learn from both successful strategies and failed attempts, continuously evolving through self-reflection and reinforcement learning. The system prevents attention dilution by compressing messy search histories into concise, actionable workflows.
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