Yancheng Zhu, Yifan Yuan · 2026 · arXiv
DOI: 10.48550/arXiv.2610.31314 · open-access PDF
대규모 언어 모델은 지침을 반복할 수 있지만 계속 순종하지 못합니다. 우리는 긴 경계 대화에서 이 추억 준수 차별화를 연구하고 State Integrity Collapse를 정의합니다: 모델은 메모리에 명시된 제한을 유지하지만 나중에 적용하는 것을 중지합니다.We introduce StateTrack-Bench, a controlled suite of conversations covering numerical, format, role/persona, factual-memory, and logical-rule constraints.Around seven provider-pinned model snapshots and five dialog-deep probes, strict joint retention ranges from 62.4% to 100.0%, with degradation concentrated in symbolic
Machine translation (Meta M2M100); the English original follows.
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
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