EvolveMem:Self-Evolving Memory Architecture via AutoResearch for LLM Agents
Summary
EvolveMem introduces a self-evolving memory architecture for LLM agents that optimizes retrieval configurations through LLM-powered diagnosis and iterative research cycles, achieving significant performance improvements on benchmarks like LoCoMo and MemBench.
View Cached Full Text
Cached at: 05/15/26, 04:23 AM
Paper page - EvolveMem:Self-Evolving Memory Architecture via AutoResearch for LLM Agents
Source: https://huggingface.co/papers/2605.13941
Abstract
EvolveMem enables adaptive memory systems for LLM agents through self-evolving retrieval mechanisms that autonomously optimize configuration parameters via diagnostic modules and iterative research cycles.
Long-term memoryis essential forLLM agentsthat operate across multiple sessions, yet existing memory systems treatretrieval infrastructureas fixed: stored content evolves while scoring functions, fusion strategies, and answer-generation policies remain frozen at deployment. We argue that truly adaptive memory requires co-evolution at two levels: the stored knowledge and the retrieval mechanism that queries it. We present EvolveMem, aself-evolving memoryarchitecture that exposes its full retrieval configuration as astructured action spaceoptimized by anLLM-powered diagnosis module. In each evolution round, the module reads per-question failure logs, identifies root causes, and proposes targeted configuration adjustments; aguarded meta-analyzerapplies them with automatic revert-on-regression and explore-on-stagnation safeguards. This closed-loop self-evolution realizes anAutoResearchprocess: the system autonomously conductsiterative research cycleson its own architecture, replacing manual configuration tuning. Starting from a minimal baseline, the process converges autonomously, discovering effectiveretrieval strategiesincluding entirely new configuration dimensions not present in the original action space. On LoCoMo, EvolveMem outperforms the strongest baseline by 25.7% relative and achieves a 78.0% relative improvement over the minimal baseline. On MemBench, EvolveMem exceeds the strongest baseline by 18.9% relative. Evolved configurations transfer across benchmarks with positive rather than catastrophic transfer, indicating that the self-evolution process captures universal retrieval principles rather than benchmark-specific heuristics. Code is available at https://github.com/aiming-lab/SimpleMem.
View arXiv pageView PDFAdd to collection
Get this paper in your agent:
hf papers read 2605\.13941
Don’t have the latest CLI?curl \-LsSf https://hf\.co/cli/install\.sh \| bash
Models citing this paper0
No model linking this paper
Cite arxiv.org/abs/2605.13941 in a model README.md to link it from this page.
Datasets citing this paper0
No dataset linking this paper
Cite arxiv.org/abs/2605.13941 in a dataset README.md to link it from this page.
Spaces citing this paper0
No Space linking this paper
Cite arxiv.org/abs/2605.13941 in a Space README.md to link it from this page.
Collections including this paper0
No Collection including this paper
Add this paper to acollectionto link it from this page.
Similar Articles
MemEvoBench: Benchmarking Memory MisEvolution in LLM Agents
MemEvoBench introduces the first benchmark for evaluating memory safety in LLM agents, measuring behavioral degradation from adversarial memory injection, noisy outputs, and biased feedback across QA and workflow tasks. The work reveals that memory evolution significantly contributes to safety failures and that static defenses are insufficient.
From Storage to Experience: A Survey on the Evolution of LLM Agent Memory Mechanisms
This survey paper proposes an evolutionary framework for LLM agent memory mechanisms, categorizing their development into three stages: storage, reflection, and experience. It analyzes core drivers such as long-range consistency and continual learning to provide design principles for next-generation agents.
RecMem: Recurrence-based Memory Consolidation for Efficient and Effective Long-Running LLM Agents
RecMem is a recurrence-based memory consolidation method for long-running LLM agents that reduces token consumption by up to 87% while improving accuracy, by only invoking LLMs when semantically similar interactions recur.
H-Mem: A Novel Memory Mechanism for Evolving and Retrieving Agent Memory via a Hybrid Structure
H-Mem is a novel memory mechanism for LLM-based agents that uses a hybrid structure combining a temporal and semantic tree with a knowledge graph to model memory evolution and improve retrieval, achieving state-of-the-art performance on QA benchmarks.
HeLa-Mem: Hebbian Learning and Associative Memory for LLM Agents
HeLa-Mem is a bio-inspired memory architecture for LLM agents that models memory as a dynamic graph using Hebbian learning dynamics, featuring episodic and semantic memory stores to improve long-term coherence. Experiments on LoCoMo show superior performance across question categories while using fewer context tokens.