UMEM: Unified Memory Extraction and Management Framework for Generalizable Memory
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 utility across clusters of semantically related queries. Extensive experiments across five benchmarks demonstrate that UMEM significantly outperforms highly competitive baselines, achieving up to a 10.67% improvement in multi-turn interactive tasks. Futhermore, UMEM maintains a monotonic growth curve during continuous evolution. Codes and models will be publicly released.
698d4a6165c0d15a6d16217c
Yongshi Ye
698d4a6165c0d15a6d16217d
Hui Jiang
698d4a6165c0d15a6d16217e
Feihu Jiang
698d4a6165c0d15a6d16217f
Tian Lan
claimed_verified
2026-02-12T13:28:18.467Z
627ca439a09edfe62239c671
https://cdn-avatars.huggingface.co/v1/production/uploads/1652335660508-noauth.jpeg
Tian Lan
GMFTBY
user
GMFTBY
698d4a6165c0d15a6d162180
Yichao Du
698d4a6165c0d15a6d162181
Biao Fu
698d4a6165c0d15a6d162182
Xiaodong Shi
698d4a6165c0d15a6d162183
Qianghuai Jia
698d4a6165c0d15a6d162184
Longyue Wang
698d4a6165c0d15a6d162185
Weihua Luo
UMEM: Unified Memory Extraction and Management Framework for Generalizable Memory
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 utility across clusters of semantically related queries. Extensive experiments across five benchmarks demonstrate that UMEM significantly outperforms highly competitive baselines, achieving up to a 10.67% improvement in multi-turn interactive tasks. Futhermore, UMEM maintains a monotonic growth curve during continuous evolution. Codes and models will be publicly released.
A unified framework for memory extraction and management in LLM-based agents that improves generalization through semantic neighborhood modeling and marginal utility rewards.
self-evolving memory Large Language Models memory extraction memory management Semantic Neighborhood Modeling GRPO marginal utility reward multi-turn interactive tasks