潜在记忆体：面向多智能体系统的潜在记忆定制技术

摘要

基於大型語言模型（LLM）的多智能體系統（MAS）展現出卓越的集體智能，其中多智能體記憶機制是實現持續適應的關鍵。然而，現有多智能體記憶設計仍受制於兩個根本性瓶頸：（i）因缺乏角色感知定制而導致的記憶同質化；（ii）過度細粒度記憶條目引發的信息過載。為突破這些限制，我們提出LatentMem——一種可學習的多智能體記憶框架，能以令牌高效的方式定制智能體專屬記憶。具體而言，LatentMem包含以輕量化形式存儲原始交互軌跡的經驗庫，以及根據檢索經驗與智能體特定上下文合成緊湊潛在記憶的記憶組合器。進一步地，我們引入潛在記憶策略優化（LMPO）算法，將任務級優化信號通過潛在記憶傳導至組合器，促使其生成緊湊且高效用的表徵。在多個基準測試和主流MAS框架上的廣泛實驗表明，LatentMem相比原始設置實現了最高19.36%的性能提升，且無需修改底層框架即可持續優於現有記憶架構。

English

Large language model (LLM)-powered multi-agent systems (MAS) demonstrate remarkable collective intelligence, wherein multi-agent memory serves as a pivotal mechanism for continual adaptation. However, existing multi-agent memory designs remain constrained by two fundamental bottlenecks: (i) memory homogenization arising from the absence of role-aware customization, and (ii) information overload induced by excessively fine-grained memory entries. To address these limitations, we propose LatentMem, a learnable multi-agent memory framework designed to customize agent-specific memories in a token-efficient manner. Specifically, LatentMem comprises an experience bank that stores raw interaction trajectories in a lightweight form, and a memory composer that synthesizes compact latent memories conditioned on retrieved experience and agent-specific contexts. Further, we introduce Latent Memory Policy Optimization (LMPO), which propagates task-level optimization signals through latent memories to the composer, encouraging it to produce compact and high-utility representations. Extensive experiments across diverse benchmarks and mainstream MAS frameworks show that LatentMem achieves a performance gain of up to 19.36% over vanilla settings and consistently outperforms existing memory architectures, without requiring any modifications to the underlying frameworks.

潜在记忆体：面向多智能体系统的潜在记忆定制技术

LatentMem: Customizing Latent Memory for Multi-Agent Systems

摘要

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