UMEM: Unified Memory Extraction and Management Framework for Generalizable Memory
Yongshi Ye, Hui Jiang, Feihu Jiang, Tian Lan, Yichao Du, Biao Fu, Xiaodong Shi, Qianghuai Jia, Longyue Wang, Weihua Luo

TL;DR
UMEM introduces a unified framework for memory extraction and management in LLM-based agents, improving generalization and performance across benchmarks by jointly optimizing memory processes and evaluating utility across semantic clusters.
Contribution
The paper presents UMEM, a novel self-evolving memory framework that jointly optimizes extraction and management, incorporating semantic neighborhood modeling for better generalization.
Findings
Achieves up to 10.67% improvement in multi-turn tasks.
Maintains monotonic growth during continuous evolution.
Outperforms state-of-the-art baselines on five benchmarks.
Abstract
Self-evolving memory serves as the trainable parameters for Large Language Models (LLMs)-based agents, where extraction (distilling insights from experience) and management (updating the memory bank) must be tightly coordinated. Existing methods predominately optimize memory management while treating memory extraction as a static process, resulting in poor generalization, where agents accumulate instance-specific noise rather than robust memories. To address this, we propose Unified Memory Extraction and Management (UMEM), a self-evolving agent framework that jointly optimizes a Large Language Model to simultaneous extract and manage memories. To mitigate overfitting to specific instances, we introduce Semantic Neighborhood Modeling and optimize the model with a neighborhood-level marginal utility reward via GRPO. This approach ensures memory generalizability by evaluating memory…
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Artificial Intelligence in Healthcare and Education
