Measuring the metacognition of AI

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.

metasignal: A Python Package for Comprehensive Metacognitive Analysis and Decision-Making

arXiv:2607. 29093v1 Announce Type: cross Abstract: Metasignal is an open-source Python package for signal detection theory (SDT) and metacognitive measurement.

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.

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.

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).

The Computational Basis of Confidence in Large Language Models

Reliable confidence -- the probability that a model's own answer is correct -- is essential for the trustworthy deployment of language models. Existing work has largely evaluated confidence by how well it predicts correctness and whether it is calibrated, leaving open a more fundamental question: what does the confidence signal itself represent?

The Computational Basis of Confidence in Large Language Models

arXiv:2607. 12447v1 Announce Type: cross Abstract: Reliable confidence -- the probability that a model's own answer is correct -- is essential for the trustworthy deployment of language models.

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.

Confidence-Building Measures for Artificial Intelligence: Workshop proceedings

TRUST-ESD: A Risk-Calibrated and Governance-Aware AI Framework for Enterprise Strategic Decision Support Under Uncertainty

arXiv:2607. 20065v1 Announce Type: new Abstract: Enterprise strategic decision support requires AI systems that are not only accurate, but also uncertainty-aware, risk-calibrated, explainable, and governance-compliant.

Quantifying Aleatoric Uncertainty of In-Context Learning for Robust Measure of LLM Prediction Confidence

arXiv:2606. 19353v1 Announce Type: cross Abstract: In-Context Learning (ICL) allows LLMs to adapt to new tasks from a few demonstrations, but its reliability remains a concern: predictions are highly sensitive to both prompt design and the model's ability to understand the context, obscuring whether failures arise from data properties or model limitations.

The "I Don't Know" Filter: Enhancing Agentic Reliability in Function Calling

arXiv:2607. 04034v1 Announce Type: cross Abstract: The language models that underpin agents have seen a rapid rise in performance on function calling benchmarks.