behavioral-relapse

This paper introduces ReBIND, a framework that measures, predicts, and repairs behavioral relapse in black-box LLM dialogues where models continue to follow revoked constraints. Experiments with HumanEval show that ahead-of-time compilation significantly reduces relapse compared to a baseline, while adaptive interventions add no detectable gain.