UMEM: Unified Memory Extraction and Management Framework for Generalizable Memory
UMEM framework improves multi-turn task performance by 10.67% through unified memory extraction and management.
Key Findings
Methodology
The UMEM framework integrates large language models with semantic neighborhood modeling and GRPO optimization to jointly optimize memory extraction and management. Core components include a frozen agent executor, a memory bank, and a learned memory optimizer. The memory optimizer evolves the memory bank by extracting reusable memories.
Key Results
- UMEM improved performance by 10.67% in multi-turn interactive tasks, significantly outperforming baselines across five benchmarks.
- In single-turn reasoning tasks, UMEM excelled on AIME and GPQA-Diamond benchmarks, with improvements of 5.0% and 2.5%, respectively.
- Ablation studies show that optimizing memory management alone leads to significant performance degradation, validating the necessity of joint optimization.
Significance
The UMEM framework addresses the bottleneck of self-evolution in large language models in dynamic environments by jointly optimizing memory extraction and management, significantly enhancing memory generalizability. This approach has important implications for academia and industry, especially in applications requiring long-term memory and self-evolution.
Technical Contribution
The UMEM framework offers new theoretical guarantees in joint optimization of memory extraction and management through semantic neighborhood modeling and marginal utility reward, ensuring memory generalizability and alignment with management policies. This method fundamentally differs from existing memory management methods, opening new engineering possibilities.
Novelty
UMEM is the first to achieve joint optimization of memory extraction and management, addressing instance-specific noise accumulation through semantic neighborhood modeling and GRPO algorithm, marking significant innovation compared to existing methods.
Limitations
- UMEM may face computational cost challenges when handling very large memory banks.
- The effectiveness of semantic neighborhood modeling depends on the quality of the pre-trained encoder.
Future Work
Future directions include optimizing UMEM's computational efficiency on large datasets and exploring more efficient semantic neighborhood modeling methods to further enhance memory generalizability.
AI Executive Summary
Memory management is crucial for the self-evolution of large language models in dynamic environments. However, existing methods often treat memory extraction as a static process, leading to the accumulation of instance-specific noise. The UMEM framework addresses this issue by jointly optimizing memory extraction and management. At its core, UMEM employs semantic neighborhood modeling and the GRPO algorithm to ensure memory generalizability by evaluating memory utility across semantically related queries. Experimental results show that UMEM improves performance by 10.67% in multi-turn interactive tasks and performs well in single-turn reasoning tasks. This framework opens new possibilities for long-term memory and self-evolution in intelligent agents. However, UMEM's computational cost when handling large memory banks remains a challenge to be optimized.
Deep Analysis
Background
Large language models have made significant advances in natural language processing, but their parameters are typically frozen after deployment, limiting their ability to learn from continuous interactions. Existing memory-augmented models primarily focus on memory management, neglecting dynamic optimization of memory extraction, resulting in insufficient memory generalizability.
Core Problem
The ability of large language models to self-evolve in dynamic environments is limited by the optimization of memory extraction and management. Existing methods often accumulate instance-specific noise, leading to memory pollution and poor generalization.
Innovation
The UMEM framework achieves joint optimization of memory extraction and management through semantic neighborhood modeling and the GRPO algorithm. This innovation not only enhances memory generalizability but also ensures consistency with management policies.
Methodology
- �� Frozen agent executor for task execution.
- �� Memory bank as external parameters storing evolvable memories.
- �� Memory optimizer evolves the memory bank by extracting reusable memories.
- �� Semantic neighborhood modeling constructs clusters of semantically related queries.
- �� GRPO algorithm optimizes memory extraction and management through marginal utility reward.
Experiments
Experiments were conducted on five benchmarks, including AIME and GPQA-Diamond, using Llama-3.2-1B-Instruct and Qwen3-4B-Instruct as memory optimizers. Baseline methods include ReMem and Memp, with evaluation metrics of success rate for multi-turn interactive tasks and accuracy for single-turn reasoning tasks.
Results
UMEM improved performance by 10.67% in multi-turn interactive tasks and performed well in single-turn reasoning tasks. Ablation studies show that joint optimization of memory extraction and management is key to performance improvement.
Applications
UMEM can be applied to intelligent agent applications requiring long-term memory and self-evolution, such as dialogue systems and autonomous driving. Its semantic neighborhood modeling method can also be applied to other tasks requiring semantic understanding.
Limitations & Outlook
UMEM may face computational cost challenges when handling large memory banks. The effectiveness of semantic neighborhood modeling depends on the quality of the pre-trained encoder, and future research can explore more efficient modeling methods.
Plain Language Accessible to non-experts
Imagine a library, and UMEM is like the librarian. Every time someone borrows a book, the librarian not only finds the most suitable book but also updates the library's collection based on the borrower's feedback. UMEM uses semantic neighborhood modeling to ensure that each borrowing record helps the librarian better manage the library's collection, avoiding the accumulation of unnecessary books.
ELI14 Explained like you're 14
Imagine UMEM as a super-smart notebook! Every time you learn something new at school, it helps you jot it down, but it doesn't just remember it simply. It summarizes the most useful points based on what you've learned before. So, when you face a similar problem next time, it can quickly give you the best answer!
Glossary
UMEM (Unified Memory Extraction and Management Framework)
A framework combining memory extraction and management to enhance memory generalizability.
Used to optimize memory management in large language models.
GRPO (Group Relative Policy Optimization)
An optimization algorithm used to evaluate memory utility in semantic neighborhoods.
Used to optimize memory extraction and management in the UMEM framework.
Semantic Neighborhood Modeling
Constructs clusters of semantically related queries to simulate cross-task variations.
Used to enhance memory generalizability.
Memory Optimizer
The core component responsible for extracting and managing memory.
A key part of the UMEM framework.
Memory Bank
Stores evolvable memories as external parameters.
Supports task execution in large language models.
Open Questions Unanswered questions from this research
- 1 How to improve UMEM's computational efficiency on large datasets?
- 2 How applicable is semantic neighborhood modeling across different tasks?
Applications
Immediate Applications
Dialogue System Optimization
UMEM can be used in dialogue systems to enhance coherence and accuracy in multi-turn conversations.
Long-term Vision
Autonomous Driving
UMEM's memory management capabilities can be used in autonomous driving to improve adaptability to complex scenarios.
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 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.