RGMem: Renormalization Group-based Memory Evolution for Language Agent User Profile

• URL: http://arxiv.org/abs/2510.16392v1
• Date: Sat, 18 Oct 2025 08:16:46 GMT
• Title: RGMem: Renormalization Group-based Memory Evolution for Language Agent User Profile
• Authors: Ao Tian, Yunfeng Lu, Xinxin Fan, Changhao Wang, Lanzhi Zhou, Yeyao Zhang, Yanfang Liu,
• Abstract summary: We propose a self-evolving memory framework, inspired by the ideology of classic renormalization group (RG) in physics.<n>This framework enables to organize the dialogue history in multiple scales.<n>The core innovation of our work lies in modeling memory evolution as a multi-scale process of information compression and emergence.
• Score: 8.224917568034572
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Personalized and continuous interactions are the key to enhancing user experience in today's large language model (LLM)-based conversational systems, however, the finite context windows and static parametric memory make it difficult to model the cross-session long-term user states and behavioral consistency. Currently, the existing solutions to this predicament, such as retrieval-augmented generation (RAG) and explicit memory systems, primarily focus on fact-level storage and retrieval, lacking the capability to distill latent preferences and deep traits from the multi-turn dialogues, which limits the long-term and effective user modeling, directly leading to the personalized interactions remaining shallow, and hindering the cross-session continuity. To realize the long-term memory and behavioral consistency for Language Agents in LLM era, we propose a self-evolving memory framework RGMem, inspired by the ideology of classic renormalization group (RG) in physics, this framework enables to organize the dialogue history in multiple scales: it first extracts semantics and user insights from episodic fragments, then through hierarchical coarse-graining and rescaling operations, progressively forms a dynamically-evolved user profile. The core innovation of our work lies in modeling memory evolution as a multi-scale process of information compression and emergence, which accomplishes the high-level and accurate user profiles from noisy and microscopic-level interactions.

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