Implementation of a Metacognition Framework for Self-Awareness and Self-Regulation in Ensembles of LLMs
- ID: 8a6bd3ae
- 原文链接: https://arxiv.org/abs/2608.15400
- PDF: https://arxiv.org/pdf/2608.15400
- 作者: Charles Courchaine, Ricky J. Sethi, Hefei Qiu
- 日期: 2026-08-15
- 更新: 2026-08-15
- 分类: learning
- 来源类型: paper
- 标签: metacognition, llm-ensemble, self-awareness, agents, arxiv
- 质量评分: 4/5
- 抓取时间: 2026-08-19T04:47:42Z
中文导读
LLM 难以评估自身不确定性发现知识冲突识别超出能力边界的问题，损害可靠性与可信度本文给出面向 LLM 集成的元认知框架首次实现，包含显式的监测与控制机制：计算'元认知状态向量'（MSV），沿源自认知心理学的五个维度量化自知力情绪反应正确性评估经验匹配信息冲突问题重要性MSV 同时驱动自调节：按查询复杂度在 System 1（快速，单/多节点）与 System 2（深思，多节点）间自动切换；System 2 执行时用图论算法按各节点的 MSV 状态分配专职角色（Domain ExpertCriticEvaluatorSynthesizerGeneralist）
为什么值得关注
LLM 集成的元认知状态向量：五维自知力量化 + System 1/2 自动切换与角色图分配
关键信息
- 论文标题: Implementation of a Metacognition Framework for Self-Awareness and Self-Regulation in Ensembles of LLMs
- 作者: Charles Courchaine, Ricky J. Sethi, Hefei Qiu
- arXiv: https://arxiv.org/abs/2608.15400
- 发布时间: 2026-08-15
- arXiv 分类: cs.AI, cs.MA
- 关联标签: metacognition, llm-ensemble, self-awareness, agents, arxiv
English Abstract
Large Language Models (LLMs) are notorious for struggling with assessing their own uncertainty, detecting knowledge conflicts, or recognizing when problems exceed their expertise; such limitations inevitably undermine reliability and trust in LLMs. In this paper, we present the first implementation of a metacognitive framework for ensembles of LLMs that addresses these challenges through explicit monitoring and control mechanisms. Our system computes a Metacognitive State Vector (MSV) quantifying self-awareness for monitoring across five dimensions derived from cognitive psychology: Emotional Response, Correctness Evaluation, Experiential Match, Conflicting Information, and Problem Importance. MSV values also provide self-regulation for control, automatically switching between System 1 (fast, single- or multi-node) and System 2 (deliberative, multi-node) processing based on query complexity. For System 2 execution, graph-theoretic algorithms control the assignment of specialized roles (Domain Expert, Critic, Evaluator, Synthesizer, and Generalist) to ensemble nodes according to their MSV-quantified metacognitive states. Our implementation allows users to explore how different query types trigger distinct processing modes. The Proof-of-Concept (PoC) demo showcases the framework with illustrative examples showing appropriate System 1/System 2 routing and helps visualize the metacognitive process via real-time radar charts and decision indicators. This PoC implementation demonstrates the feasibility of creating a framework for metacognitive self-awareness and self-regulation in LLM systems.
English Summary
LLMs struggle to assess their own uncertainty, detect knowledge conflicts, or recognize when problems exceed their expertise, undermining reliability and trust. This paper presents the first implementation of a metacognitive framework for ensembles of LLMs with explicit monitoring and control mechanisms. It computes a Metacognitive State Vector (MSV) quantifying self-awareness across five dimensions derived from cognitive psychology: Emotional Response, Correctness Evaluation, Experiential Match, Conflicting Information, and Problem Importance....
Obsidian Notes
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