Self-Evolving Multi-Agent Systems via Decentralized Memory

TL;DR

DecentMem introduces a decentralized memory framework for multi-agent systems, enabling agents to improve continually with reduced communication overhead and enhanced privacy, backed by theoretical guarantees and empirical performance gains.

Contribution

It proposes a novel decentralized memory design with dual pools per agent, providing theoretical guarantees and significant empirical improvements over centralized and no-memory baselines.

Findings

DecentMem improves average accuracy by up to 23.8% over centralized memory baselines.

DecentMem reduces token usage by up to 49%.

Theoretical analysis shows $O(\log T)$ regret, matching stochastic bandit bounds.

Abstract

Self-evolving multi-agent systems (MAS) have emerged as a promising route to LLM agents that continually improve from experience, with persistent memory at their foundation. However, existing designs almost exclusively adopt a centralized repository shared across agents, incurring communication and coordination overhead, raising privacy concerns, and collapsing agent diversity. We propose DecentMem, a decentralized memory framework in which each agent maintains its own dual-pool memory -- an exploitation pool of consolidated past trajectories and an exploration pool of LLM-generated candidates for unseen contexts. The two pools are reweighted online based on stage-wise feedback from an LLM-as-a-judge. Theoretically, we prove that this design guarantees global reachability of the solution space and achieves $O(\log T)$ cumulative regret, matching the stochastic bandit lower bound up to constants. In practice, across three MAS frameworks (AutoGen, DyLAN, AgentNet), three Qwen3 backbones (4B/8B/14B), two Gemma4 backbones (E2B/E4B) and five benchmarks spanning math, code, QA, and embodied tasks, DecentMem improves average accuracy by up to 23.8% over the strongest centralized memory baseline and by up to 52.5% over the no-memory baseline, while reducing token usage by up to 49%.