@inproceedings{fu-etal-2026-latentmem,
title = "{L}atent{M}em: Customizing Latent Memory for Multi-Agent Systems",
author = "Fu, Muxin and
Xue, Xiangyuan and
Li, Yafu and
He, Zefeng and
Huang, Siyuan and
Qu, Xiaoye and
Cheng, Yu and
Yang, Yang",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.457/",
pages = "10512--10533",
ISBN = "979-8-89176-459-0",
abstract = "Large language model (LLM)-powered multi-agent systems (MAS) demonstrate remarkable collective intelligence, with multi-agent memory serving as a pivotal mechanism for continual adaptation. However, existing multi-agent memory designs remain constrained by two fundamental bottlenecks: (i) memory homogenization caused by limited role-aware customization, and (ii) information overload induced by exposing agents to excessively fine-grained textual memories. To address these limitations, we propose LatentMem, a learnable multi-agent memory framework that customizes agent-specific experiential memories while reducing textual context. Specifically, LatentMem comprises an experience bank for lightweight storage of raw interaction trajectories 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 learn compact, high-utility latent representations. Extensive experiments across six benchmarks and four mainstream MAS frameworks show that LatentMem improves vanilla settings by up to 19.36{\%} and consistently outperforms existing memory architectures, without requiring modifications to the agent-layer workflows."
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<abstract>Large language model (LLM)-powered multi-agent systems (MAS) demonstrate remarkable collective intelligence, with multi-agent memory serving as a pivotal mechanism for continual adaptation. However, existing multi-agent memory designs remain constrained by two fundamental bottlenecks: (i) memory homogenization caused by limited role-aware customization, and (ii) information overload induced by exposing agents to excessively fine-grained textual memories. To address these limitations, we propose LatentMem, a learnable multi-agent memory framework that customizes agent-specific experiential memories while reducing textual context. Specifically, LatentMem comprises an experience bank for lightweight storage of raw interaction trajectories 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 learn compact, high-utility latent representations. Extensive experiments across six benchmarks and four mainstream MAS frameworks show that LatentMem improves vanilla settings by up to 19.36% and consistently outperforms existing memory architectures, without requiring modifications to the agent-layer workflows.</abstract>
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%0 Conference Proceedings
%T LatentMem: Customizing Latent Memory for Multi-Agent Systems
%A Fu, Muxin
%A Xue, Xiangyuan
%A Li, Yafu
%A He, Zefeng
%A Huang, Siyuan
%A Qu, Xiaoye
%A Cheng, Yu
%A Yang, Yang
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F fu-etal-2026-latentmem
%X Large language model (LLM)-powered multi-agent systems (MAS) demonstrate remarkable collective intelligence, with multi-agent memory serving as a pivotal mechanism for continual adaptation. However, existing multi-agent memory designs remain constrained by two fundamental bottlenecks: (i) memory homogenization caused by limited role-aware customization, and (ii) information overload induced by exposing agents to excessively fine-grained textual memories. To address these limitations, we propose LatentMem, a learnable multi-agent memory framework that customizes agent-specific experiential memories while reducing textual context. Specifically, LatentMem comprises an experience bank for lightweight storage of raw interaction trajectories 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 learn compact, high-utility latent representations. Extensive experiments across six benchmarks and four mainstream MAS frameworks show that LatentMem improves vanilla settings by up to 19.36% and consistently outperforms existing memory architectures, without requiring modifications to the agent-layer workflows.
%U https://aclanthology.org/2026.emnlp-main.457/
%P 10512-10533
Markdown (Informal)
[LatentMem: Customizing Latent Memory for Multi-Agent Systems](https://aclanthology.org/2026.emnlp-main.457/) (Fu et al., EMNLP 2026)
ACL
- Muxin Fu, Xiangyuan Xue, Yafu Li, Zefeng He, Siyuan Huang, Xiaoye Qu, Yu Cheng, and Yang Yang. 2026. LatentMem: Customizing Latent Memory for Multi-Agent Systems. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 10512–10533, Budapest, Hungary. Association for Computational Linguistics.