Metacognition in LLMs: Foundations, Progress, and Opportunities
- Published
- Source
- arXiv
- Paper number
- 607
- Field
- LLMs / NLP
- arXiv ID
- 2607.11881
Key points
- It defines metacognition as a loop of monitoring, meaning state evaluation, and control, meaning strategy adjustment, and uses that lens to classify LLM research.
- It organizes a range of metacognitive measurement methods, including psychology-based, neurofeedback-based, confidence-based, and interpretability-based approaches.
- The literature shows that metacognition helps reduce hallucination, improve reliability, and improve human-AI collaboration.
- It analyzes metacognitive applications in reasoning models, agents, multi-agent systems, memory, retrieval, and tool use.
- It systematically studies the effects of model size, post-training, and sampling temperature on metacognitive ability.
- It identifies gaps in metacognitive evaluation and proposes future research directions, including domain generality and architecture design.
Paper links
External research summaries. These are not HDATF publications or measured product results.