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arXiv · 2610.38573

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

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A memory can be outdated for present-day reasoning yet indispensable for reconstructing the past.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 Historical-reconstruction experiments demonstrate that preserving superseded records supports accurate earlier-state reasoning without compromising current-state answers.

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Chang Li, Xianhao Ou, Xinyan Ma, Hao Li, Haoyuan Xu, Jiafeng Liang, Xiaohan Wang, Jiajun Chai, Guojun Yin, Ming Liu. 2026-10-28. CALM: Conflict-Aware Lifecycle Memory Governance for Long-Horizon Language Agents. https://arxiv.org/abs/2610.38573

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