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New CALM framework enhances language agent memory for long-horizon tasks

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced CALM, a novel framework designed to manage memory for long-horizon language agents. CALM addresses the challenge of balancing outdated yet essential historical information with current reasoning needs by decoupling memory preservation from query-time admission. The system converts records into evidence-grounded memory cells, updating their validity and lifecycle states based on support, conflict, and dependency signals. Experiments using Qwen and GPT backbones on the LongMemEval-S and LoCoMo benchmarks show CALM surpasses existing methods in accuracy and improves evidence selection through query-conditioned admission. AI

IMPACT This framework could improve the ability of AI agents to maintain context and perform complex, long-term tasks.

RANK_REASON The cluster contains a research paper detailing a new framework for language agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New CALM framework enhances language agent memory for long-horizon tasks

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The cluster contains a research paper detailing a new framework for language agents. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Chang Li, Xianhao Ou, Xinyan Ma, Hao Li, Haoyuan Xu, Jiafeng Liang, Xiaohan Wang, Jiajun Chai, Guojun Yin, Ming Liu ·

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

    arXiv:2610.38573v1 Announce Type: new Abstract: A memory can be outdated for present-day reasoning yet indispensable for reconstructing the past.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 d…

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