CALM: Conflict-Aware Lifecycle Memory Governance for Long-Horizon Language Agents

Authors: Chang Li , Xianhao Ou , Xinyan Ma , Hao Li , Haoyuan Xu , Jiafeng Liang , Xiaohan Wang , Jiajun Chai , Guojun Yin , Ming Liu

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Abstract: A memory can be outdated for present-day reasoning yet indispensable for reconstructing the past. Relevance-based retrieval overlooks this tension: semantic relevance, evidence-grounded validity, and query-specific eligibility are distinct. We propose CALM, a conflict-aware lifecycle memory governance framework that decouples persistent memory preservation from query-time admission. CALM converts heterogeneous records into evidence-grounded memory cells, updates validity and lifecycle states using support, conflict, and dependency signals, and admits query-appropriate evidence through training-free or learned gating informed by temporal applicability. Across LongMemEval-S and LoCoMo with Qwen and GPT backbones, CALM outperforms state-of-the-art baselines, achieving the best overall accuracy. Compared with simple filtering and reranking, query-conditioned admission further improves evidence selection. Conflict-blocking and robustness analyses show CALM suppresses erroneous retrieved memories while retaining useful evidence. Historical-reconstruction experiments demonstrate that preserving superseded records supports accurate earlier-state reasoning without compromising current-state answers.

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Artificial Intelligence (cs.AI)

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arXiv:2610.38573 [cs.AI]

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arXiv:2610.38573v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.38573

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From: Chang Li [ view email ]

[v1]

Wed, 28 Oct 2026 10:06:57 UTC (671 KB)

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