Dead text or binding clause? Measuring and restoring constraint influence in black-box LLM dialogues

Multi-turn dialogues let users revoke constraints as easily as impose them, but revocation does not reliably take effect: models keep enacting withdrawn requirements (occasionally beneath comments asserting their removal), a failure we call behavioral relapse, or revocation inertia. No existing instrument measures this influence per clause, predicts it before delivery, or repairs it under matched budgets. sysname closes the three gaps through the model API alone: a contract ledger pairs every constraint with an executable checker, records revocations as tombstones, and compiles the net constraint state ahead of time into a single specification; a sequential ablation probe measures per-clause adherence and incremental behavioral effect; a repair ladder operates under token- and attempt-matched budgets. On dataname (NTasks HumanEval tasks, NClauses verified checkers), relapse at an 8B operating point climbs from ScaleDelayedMTwo to ScaleDelayedMEight as constraint load grows, while stronger models sit at floor. Under matched checkers, model, and budget, ahead-of-time compilation significantly reduces relapse against a no-ledger verifier-retry baseline (RestoreDiff, 95% CI RestoreDiffCI, p RestoreDiffP); adaptive ladder interventions stacked on top add no detectable gain (95% confidence excludes gains ≥ LadderExcludedGain). The probe predicts relapse before delivery (AUROC AurocPrimary); a one-sentence tombstone note recovers about a third of the compilation effect and survives a placebo control. At CostDeliveryFactor delivery overhead and CostTotalHedged of API compute for every result, revocation failure becomes a measurable, predictable, and repairable property of dialogue state rather than an invisible one.