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

arXiv:2608.12599v1 Announce Type: new Abstract: 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users revoke constraints as easily as impose them, but revocation does not reliably take effect: models keep enacting withdrawn requirements (occasionally beneath comments asserting their removal), a failure we call behavioral relapse, or revocation inertia.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 ataname{} (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 geq 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.

Source: arXiv cs.AI | 2026-08-14

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