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English(EN) CALM: Conflict-Aware Lifecycle Memory Governance for Long-Horizon Language Agents

新的CALM框架增强了语言代理在长时程任务中的记忆能力

研究人员推出了一种新颖的CALM框架,旨在管理长时程语言代理的内存。CALM通过将内存保存与查询时准入分离,解决了过时但至关重要的历史信息与当前推理需求之间的平衡挑战。该系统将记录转换为基于证据的内存单元,并根据支持、冲突和依赖信号更新其有效性和生命周期状态。使用Qwen和GPT骨干在LongMemEval-S和LoCoMo基准上的实验表明,CALM在准确性方面优于现有方法,并通过查询条件准入提高了证据选择能力。 AI

影响 该框架可以提高AI代理保持上下文和执行复杂、长期任务的能力。

排序理由 该集群包含一篇详细介绍语言代理新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新的CALM框架增强了语言代理在长时程任务中的记忆能力

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该集群包含一篇详细介绍语言代理新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [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:面向长时域语言代理的冲突感知生命周期内存治理

    arXiv:2610.38573v1 Announce Type: new Abstract: A memory can be outdated for present-day reasoning yet indispensable for reconstructing the past.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