When LLMs Forget What They Believe: Characterizing State Integrity Collapse in Long-Horizon Dialogues
Large language models can repeat an instruction while failing to keep obeying it. We study this recall-compliance dissociation in long-horizon dialogue and define State Integrity Collapse: a model preserves a stated constraint in memory but stops applying it to later turns. We introduce StateTrack-Bench, a controlled suite of conversations covering numerical, format, role/persona, factual-memory, and logical-rule constraints. Across seven provider-pinned model snapshots and five dialogue-depth probes, strict joint retention ranges from 62.4% to 100.0%, with degradation concentrated in symbolic constraints and in one especially brittle model. A forked recall audit shows that 88.6% of obser...
| Authors | Yancheng Zhu, Yifan Yuan |
|---|---|
| Primary category | cs.CL |
| Published | 22 October 2026 |
| Last updated at source | 22 October 2026 |
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