arXiv 2510.163922026-06-02cs.AI

RGMem：基于重正化群启发的语言智能体记忆演化

RGMem: Renormalization Group-inspired Memory Evolution for Language Agents

School of Computer Science；Engineering, Beihang University, Beijing, China；School of Reliability；Systems Engineering, Beihang University, Beijing, China；State Key Laboratory of Complex \& Critical Software Environment；National Key Laboratory of Reliability；State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences

Ao Tian, Yunfeng Lu, Xinxin Fan, Changhao Wang, Lanzhi Zhou, Yeyao Zhang, Yanfang Liu

备注：: Accepted to ICML 2026

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AI总结：

提出RGMem框架，利用重正化群思想对长期对话记忆进行多尺度粗粒化、阈值更新和重缩放，实现从事实到用户偏好的层次化整合，在LOCOMO和PersonaMem基准上超越现有记忆系统。

摘要：

个性化和持续交互对于基于LLM的对话智能体至关重要，但有限的上下文窗口和静态参数记忆阻碍了对长期、跨会话用户状态的建模。现有方法（包括检索增强生成和显式记忆系统）主要在事实层面操作，难以从演化且可能冲突的对话中提炼稳定的偏好和深层用户特征。为应对这一挑战，我们提出RGMem，一种受重正化群（RG）多尺度组织和涌现观点启发的自演化记忆框架。RGMem将长期对话记忆建模为多尺度演化过程：情节交互被转化为语义事实和用户洞察，然后通过层次化粗粒化、阈值更新和重缩放逐步整合为动态演化的用户画像。通过明确分离快速变化的证据和慢变特征，并启用非线性、相变般的动力学，RGMem实现了超越平面检索或静态摘要的稳健个性化。在LOCOMO和PersonaMem基准上的大量实验表明，RGMem持续优于最先进的记忆系统，实现了更强的跨会话连续性并更好地适应演化的用户偏好。代码可在https://github.com/fenhg297/RGMem获取。

英文摘要：

Personalized and continuous interactions are critical for LLM-based conversational agents, yet finite context windows and static parametric memory hinder the modeling of long-term, cross-session user states. Existing approaches, including retrieval-augmented generation and explicit memory systems, primarily operate at the fact level, making it difficult to distill stable preferences and deep user traits from evolving and potentially conflicting dialogues.To address this challenge, we propose RGMem, a self-evolving memory framework inspired by the renormalization group (RG) perspective on multi-scale organization and emergence. RGMem models long-term conversational memory as a multi-scale evolutionary process: episodic interactions are transformed into semantic facts and user insights, which are then progressively integrated through hierarchical coarse-graining, thresholded updates, and rescaling into a dynamically evolving user profile.By explicitly separating fast-changing evidence from slow-varying traits and enabling non-linear, phase-transition-like dynamics, RGMem enables robust personalization beyond flat retrieval or static summarization. Extensive experiments on the LOCOMO and PersonaMem benchmarks demonstrate that RGMem consistently outperforms SOTA memory systems, achieving stronger cross-session continuity and improved adaptation to evolving user preferences. Code is available at https://github.com/fenhg297/RGMem

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。