Black-Box Reliability Certification for AI Agents via Self-Consistency Sampling and Conformal Calibration

• URL: http://arxiv.org/abs/2602.21368v1
• Date: Tue, 24 Feb 2026 21:03:50 GMT
• Title: Black-Box Reliability Certification for AI Agents via Self-Consistency Sampling and Conformal Calibration
• Authors: Charafeddine Mouzouni,
• Abstract summary: We answer with a reliability level -- a single number per system-task pair.<n>Self-consistency sampling reduces uncertainty exponentially.<n> conformal calibration guarantees correctness within 1/(n+1) of the target level.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Given a black-box AI system and a task, at what confidence level can a practitioner trust the system's output? We answer with a reliability level -- a single number per system-task pair, derived from self-consistency sampling and conformal calibration, that serves as a black-box deployment gate with exact, finite-sample, distribution-free guarantees. Self-consistency sampling reduces uncertainty exponentially; conformal calibration guarantees correctness within 1/(n+1) of the target level, regardless of the system's errors -- made transparently visible through larger answer sets for harder questions. Weaker models earn lower reliability levels (not accuracy -- see Definition 2.4): GPT-4.1 earns 94.6% on GSM8K and 96.8% on TruthfulQA, while GPT-4.1-nano earns 89.8% on GSM8K and 66.5% on MMLU. We validate across five benchmarks, five models from three families, and both synthetic and real data. Conditional coverage on solvable items exceeds 0.93 across all configurations; sequential stopping reduces API costs by around 50%.

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