基于认知神经科学启发的层次化元认知监控深度搜索

摘要

基于大语言模型的深度搜索智能体已在多步检索、推理及长程任务执行方面展现出强大能力。然而在实际应用中，其失败往往源于缺乏随着任务在不确定性下演进时，对推理与检索状态进行监控与调节的机制。认知神经科学研究表明，人类元认知具有分层结构，能够将快速异常检测与选择性触发的经验驱动反思相结合。本研究提出具有元认知监控的深度搜索框架（DS-MCM），该框架通过显式分层元认知监控机制增强深度搜索能力。DS-MCM集成两大组件：快速一致性监控器负责轻量级校验外部证据与内部推理置信度的对齐情况；慢速经验驱动监控器则选择性激活，基于历史智能体轨迹的经验记忆指导纠偏干预。通过将监控机制直接嵌入推理-检索循环，DS-MCM既能判定干预时机，又能依据先验经验制定纠偏策略。在多个深度搜索基准测试及不同骨干模型上的实验表明，DS-MCM能持续提升性能表现与系统鲁棒性。

English

Deep search agents powered by large language models have demonstrated strong capabilities in multi-step retrieval, reasoning, and long-horizon task execution. However, their practical failures often stem from the lack of mechanisms to monitor and regulate reasoning and retrieval states as tasks evolve under uncertainty. Insights from cognitive neuroscience suggest that human metacognition is hierarchically organized, integrating fast anomaly detection with selectively triggered, experience-driven reflection. In this work, we propose Deep Search with Meta-Cognitive Monitoring (DS-MCM), a deep search framework augmented with an explicit hierarchical metacognitive monitoring mechanism. DS-MCM integrates a Fast Consistency Monitor, which performs lightweight checks on the alignment between external evidence and internal reasoning confidence, and a Slow Experience-Driven Monitor, which is selectively activated to guide corrective intervention based on experience memory from historical agent trajectories. By embedding monitoring directly into the reasoning-retrieval loop, DS-MCM determines both when intervention is warranted and how corrective actions should be informed by prior experience. Experiments across multiple deep search benchmarks and backbone models demonstrate that DS-MCM consistently improves performance and robustness.

基于认知神经科学启发的层次化元认知监控深度搜索

Deep Search with Hierarchical Meta-Cognitive Monitoring Inspired by Cognitive Neuroscience

摘要

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