Meta-calibration develops a reliability profile for AI agents, enhancing their self-awareness and adaptability.
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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Cros et al. (2026) studied this question.
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