Memory Intelligence Agent
- Published
- Source
- arXiv
- Paper number
- 137
- Field
- Agents / Memory
- arXiv ID
- 2604.04503
Key points
- Existing memory systems for Deep Research Agents primarily rely on long-context memory, which leads to attention dilution, noise injection, and high storage and compute costs.
- Most existing memory systems capture knowledge-oriented memory, or what, but fail to use process-oriented memory and conceptual knowledge, or how, which are central for guiding future planning in deep research.
- The poor planning and execution of DRA come from the fact that, when memory is used only for few-shot Chain-of-Thought (CoT), the planner lacks task-specific learning and the executor misreads instructions.
- MIA proposes a Manager-Planner-Executor architecture that separates nonparametric memory storage of past search trajectories from parametric planning and execution, and it uses a hybrid retrieval strategy.
- A two-stage alternating reinforcement learning (RL) strategy promotes synergistic co-evolution between the Planner and the Executor, ensuring alignment between high-level planning and effective low-level task execution.
- Test-Time Learning (TTL) and a new unsupervised evaluation framework enable continuous self-evolution, allowing the agent to refine both nonparametric and parametric memory online without explicit supervision.
Paper links
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