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CALM: Conflict-Aware Lifecycle Memory Governance for Long-Horizon Language Agents
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First announced by arXiv: 2026-10-30 · Indexed by Pub-AI: 2026-10-30 UTC
CALM: Conflict-Aware Lifecycle Memory Governance for Long-Horizon Language Agents
First announced by arXiv: 2026-10-30 · Original record ↗
- arxiv
- 2610.38573
Explicit source versions (1)
- v1 · Date not supplied
- Preprint available
Source date: 2026-10-30 · First detected: 2026-10-30 UTC · Historical baseline
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- Official evidence ↗ · RSS guid=oai:arXiv.org:2610.38573v1; arxiv:announce_type=new; item pubDate (official metadata)
Abstract · arxiv
A memory can be outdated for present-day reasoning yet indispensable for reconstructing the past.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 Historical-reconstruction experiments demonstrate that preserving superseded records supports accurate earlier-state reasoning without compromising current-state answers.