arXiv AI August 18, 2026 By Charles Courchaine, Ricky J. Sethi, Hefei Qiu

Implementation of a Metacognition Framework for Self-Awareness and Self-Regulation in Ensembles of LLMs

arXiv:2608. 15400v1 Announce Type: new 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

llms

arXiv AI

Jul 14

Metacognition in LLMs: Foundations, Progress, and Opportunities

arXiv:2607. 11881v1 Announce Type: cross Abstract: Metacognition is a foundational component of intelligence critical to effective learning, problem solving, decision-making, communication, and more.

By Gabrielle Kaili-May Liu, Areeb Gani, Jacqueline Lu, Jordan Thomas, Mark Steyvers, Arman Cohan

llms nlp benchmarks

arXiv AI

Jul 1

Reinforcement Learning with Metacognitive Feedback Elicits Faithful Uncertainty Expression in LLMs

arXiv:2606. 32032v1 Announce Type: cross Abstract: Metacognition is a critical component of intelligence that describes the ability to monitor and regulate one's own cognitive processes.

By Gabrielle Kaili-May Liu, Avi Caciularu, Gal Yona, Idan Szpektor, Arman Cohan

llms reinforcement-learning benchmarks safety

arXiv AI

Jul 9

Measuring the metacognition of AI

arXiv:2603. 29693v3 Announce Type: replace Abstract: A robust decision-making process must take into account uncertainty, especially when the choice involves inherent risks.

By Richard Servajean, Philippe Servajean

llms

arXiv AI

Jun 3

Decomposing and Measuring Evaluation Awareness

arXiv:2605. 23055v2 Announce Type: replace-cross Abstract: Frontier language models sometimes recognize that they are being evaluated and adjust their behavior, undermining validity of benchmark results.

By Changling Li, Terry Jingchen Zhang, Jie Zhang, Zhijing Jin, Sahar Abdelnabi, Maksym Andriushchenko

llms benchmarks safety

arXiv AI

Jun 16

Metacognitive Myopia in Large Language Models

arXiv:2408. 05568v2 Announce Type: replace Abstract: Large Language Models (LLMs) exhibit potentially harmful biases that reinforce culturally embedded stereotypes, influence moral judgments, or amplify positive evaluations of majority groups.

By Florian Scholten, Tobias R. Rebholz, Mandy H\"utter

llms rag agents

arXiv AI

Jul 15

Do LLMs Know What They Know? Measuring Metacognitive Efficiency with Signal Detection Theory

arXiv:2603. 25112v2 Announce Type: replace-cross Abstract: Standard evaluation of LLM confidence relies on calibration metrics (ECE, Brier score) that conflate how much a model knows (Type-1 accuracy) with how well its confidence signal tracks that knowledge (Type-2 metacognitive sensitivity).

By Jon-Paul Cacioli

llms

Related stories

Metacognition in LLMs: Foundations, Progress, and Opportunities

Reinforcement Learning with Metacognitive Feedback Elicits Faithful Uncertainty Expression in LLMs

Measuring the metacognition of AI

Decomposing and Measuring Evaluation Awareness

Metacognitive Myopia in Large Language Models

Do LLMs Know What They Know? Measuring Metacognitive Efficiency with Signal Detection Theory