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arXiv Computation and Language By Yancheng Zhu, Yifan Yuan

When LLMs Forget What They Believe: Characterizing State Integrity Collapse in Long-Horizon Dialogues

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The paper introduces the concept of State Integrity Collapse, where large language models remember a constraint but fail to apply it in later dialogue turns. It presents StateTrack‑Bench, a suite of controlled conversations testing numerical, format, role/persona, factual‑memory, and logical‑rule constraints across multiple model snapshots and dialogue depths. The study finds that while strict joint retention can be high, failures are largely due to behavioral drift rather than simple forgetting, and that reliability must be assessed over long‑horizon dialogues rather than single‑turn instruction following.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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