Bounded Confidence Envelopes for Large Language Model Inference

Abstract

Large Language Models deployed in regulated or safety-critical settings generate outputs that carry inherent uncertainty. Current mitigation strategies — prompt engineering, RLHF, retrieval-augmented generation — improve average-case quality but provide no deterministic enforcement guarantees. We introduce Bounded Confidence Envelopes (BCE), a governance mechanism that wraps LLM inference with dual-threshold confidence gating, hysteresis-based mode management, and hash-chained evidence logging. The system enforces four graduated actions — Allow, Flag, Constrain, Block — based on real-time confidence scoring, with all enforcement decisions cryptographically logged to a tamper-evident audit trail. We provide a formal specification in TLA+ covering 32.8 million states with zero counterexamples across eight safety properties. Benchmark evaluation on TruthfulQA demonstrates +2.9 percentage-point accuracy improvement and 73% filtering of incorrect outputs, with 4.2% token savings from constrained inference.