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Governance-State Corruption in Long-Term Memory for LLM Agents: A Cross-Model Stress Test

Bowen Liu, Youting Wang, Bonan Shen, Dingyan Shang · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.30698 · cited by 0 works (OpenAlex) · open-access PDF

Korean

LLM 에이전트는 메모리를 올바르게 복구할 수 있지만 유효성 상태가 변경된 후에 잘못 사용합니다. 우리는 통제 상태의 부패를 연구합니다: 보존된 레코드가 모델 표시된 컨텍스트에 들어가고 저장된 콘텐츠와 작업이 고정되는 동안 보존된 레코드가 변경되는 오류. 6개 공급자들 사이에서 731개의 벤치마크 기반 기지에서 2,080개의 일치된 오류 쌍을 생성합니다. 오프라인 제어 플랜 체크에서 부패는 96.25%의 쌍에서 운영 프로젝션을 변경하며, 오라클로 지원된 집행은 그것을 100%로 복원합니다. 후속 평가를 위해, 결정적으로 선택된, 비 확률적 인 LongEval/Memora 패널은 300개의 일치된 삼각형

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

Abstract

An LLM agent can retrieve a memory correctly yet use it incorrectly after its validity state changes. We study governance-state corruption: faults that alter which retained records enter model-visible context while stored content and tasks remain fixed. Across six providers, 731 benchmark-grounded bases yield 2,080 matched fault pairs. In offline control-plane checks, corruption changes the operative projection in 96.25% of pairs, while oracle-backed enforcement restores it in 100%. For downstream evaluation, a deterministically selected, non-probability LongMemEval/Memora panel resolves 298 of 300 matched triplets (238 distinct bases). Clean-minus-corrupted contrasts are 40.54 percentage points in LongMemEval binary correctness (95% base-cluster interval [30.32, 50.71]) and 30.60 points in rescaled Memora FAMA ([23.31, 37.69]); both sources also show recovery. OAKS Novel native option parity holds for all 6,714 resolved outputs. Full-cohort frozen-scorer corruption/recovery effects (-27.55/+27.51 points) remain descriptive proxies, not validated native semantic population effects. These bounded findings motivate evaluating memory control-state integrity alongside retrieval accuracy.

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Topics: Correctness, Population, Computer science, Context (archaeology), Econometrics, Psychology

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