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

Yancheng Zhu, Yifan Yuan · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.31314 · cited by 0 works (OpenAlex) · open-access PDF

Portuguese

Os modelos de linguagem de grande confiança podem repetir uma instrução enquanto não continuam obedecendo a ela. Estudamos esta dissociação de conformidade de recall em diálogo de longo horizonte e definimos o Colapso de Integridade do Estado: um modelo preserva uma restrição declarada na memória, mas pára de aplicá-la em turnos posteriores. Introduzimos o StateTrack-Bench, um conjunto controlado de conversas que cobre as restrições numéricas, formato, papel/pessoa, memória factual e restrições de regras lógicas. Ao longo de sete snapshots do modelo de fornecedor e cinco sondas de profundidade de diálogo, a retenção conjunta rigorosa varia de 62,4% a 100,0%, com a degradação concentrada em restrições simbólicas e em um modelo

Machine translation (Meta M2M100); the English original follows.

Abstract

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 observed failures are behavioral-drift failures rather than ordinary forgetting; excluding the brittle outlier preserves this diagnosis (84.4% of failures). Extended evaluations at depths up to 50 turns confirm that the dissociation persists beyond the core benchmark, while Structured State Anchoring recovers up to 24.4 points on immutable constraints. The results show that reliability cannot be inferred from single-turn instruction following alone; evaluation must track whether remembered constraints remain operational as dialogue state grows. Code and artifacts are available at: https://github.com/xzzvsxd/StateTrack.

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Topics: Computer science, Suite, State (computer science), Computer security, Witness, Audit

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