What question did this study set out to answer?

To address the issue of large language models hallucinating by developing a method for reliability assessment.

February 27, 2026Open Access

Meta-Calibration: When AI Agents Know Their Own Limits

Key Points

To address the issue of large language models hallucinating by developing a method for reliability assessment.
Proposed meta-calibration approach to build empirical reliability profiles for AI agents.
Utilized accumulated feedback to create a structured reliability file called CALIBRATION.md.
Implemented the reliability profile in active context for dynamic behavior adaptation.
Meta-calibration offers a domain-specific reliability framework for AI agents.
The approach improves adaptability without additional computational costs.
It contrasts with traditional methods like temperature calibration and RLHF.

Abstract

Large language models hallucinate because they have no map of their own competencies. We propose meta-calibration: an empirical reliability profile per domain, built through accumulated feedback, stored in a structured file (CALIBRATION.md), and loaded into active context at each session. This profile enables the agent to adapt its behavior based on its measured reliability. Unlike temperature calibration, RLHF, or binary guardrails, meta-calibration is readable, auditable, domain-specific, and requires zero additional computational cost. Also available in French: Méta-calibration : quand les agents IA connaissent leurs propres limites

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Cite This Study

Cros et al. (Sun,) studied this question.

synapsesocial.com/papers/69a1350eed1d949a99abe9cf https://doi.org/https://doi.org/10.5281/zenodo.18770858

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