@inproceedings{zhang-etal-2026-latcom,
title = "{L}at{C}om: Cross-Agent Latent Compression for Efficient Multi-Agent Collaboration",
author = "Zhang, Shinan and
Zhang, Tao and
Zhu, Qihui and
Zhang, Mengjie and
Jin, Dong and
Hou, Yunpeng and
Chen, Shuangwu and
Tan, Xiaobin and
Zheng, Quan and
Jianyang",
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.1068/",
pages = "24531--24547",
ISBN = "979-8-89176-459-0",
abstract = "LLM-based multi-agent systems (MAS) increasingly use latent collaboration to avoid the information loss and repeated encoding-decoding overhead of natural-language communication. However, directly forwarding all sender latents makes the receiver-side context scale with both the number of agents and the reasoning length, increasing computation, memory usage, and collaboration latency. A natural solution is latent compression. But we find that cross-agent redundancy remains unresolved in existing latent compression approaches, which typically compress each sender independently and then concatenate the results. We propose LatCom, a cross-agent latent compression framework for efficient multi-agent latent collaboration. LatCom maps multiple sender latents into a fixed number of receiver-readable and task-relevant slots. Rather than reconstructing all sender hidden states, it optimizes the compressed latents for receiver-side task utility. LatCom trains the compressor in two stages: single-sender readability learning first establishes a latent interface interpretable by the frozen receiver, and multi-sender fusion learning then trains the compressor to fuse complementary evidence and remove redundancy across agents. Experiments on multiple benchmarks with the Qwen-4B model show that LatCom improves average inference speed by 2.46x over LatentMAS, reduces token usage by 70.3{\%}, and increases average accuracy by 0.46{\%}."
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<abstract>LLM-based multi-agent systems (MAS) increasingly use latent collaboration to avoid the information loss and repeated encoding-decoding overhead of natural-language communication. However, directly forwarding all sender latents makes the receiver-side context scale with both the number of agents and the reasoning length, increasing computation, memory usage, and collaboration latency. A natural solution is latent compression. But we find that cross-agent redundancy remains unresolved in existing latent compression approaches, which typically compress each sender independently and then concatenate the results. We propose LatCom, a cross-agent latent compression framework for efficient multi-agent latent collaboration. LatCom maps multiple sender latents into a fixed number of receiver-readable and task-relevant slots. Rather than reconstructing all sender hidden states, it optimizes the compressed latents for receiver-side task utility. LatCom trains the compressor in two stages: single-sender readability learning first establishes a latent interface interpretable by the frozen receiver, and multi-sender fusion learning then trains the compressor to fuse complementary evidence and remove redundancy across agents. Experiments on multiple benchmarks with the Qwen-4B model show that LatCom improves average inference speed by 2.46x over LatentMAS, reduces token usage by 70.3%, and increases average accuracy by 0.46%.</abstract>
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%0 Conference Proceedings
%T LatCom: Cross-Agent Latent Compression for Efficient Multi-Agent Collaboration
%A Zhang, Shinan
%A Zhang, Tao
%A Zhu, Qihui
%A Zhang, Mengjie
%A Jin, Dong
%A Hou, Yunpeng
%A Chen, Shuangwu
%A Tan, Xiaobin
%A Zheng, Quan
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%A Jianyang
%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 zhang-etal-2026-latcom
%X LLM-based multi-agent systems (MAS) increasingly use latent collaboration to avoid the information loss and repeated encoding-decoding overhead of natural-language communication. However, directly forwarding all sender latents makes the receiver-side context scale with both the number of agents and the reasoning length, increasing computation, memory usage, and collaboration latency. A natural solution is latent compression. But we find that cross-agent redundancy remains unresolved in existing latent compression approaches, which typically compress each sender independently and then concatenate the results. We propose LatCom, a cross-agent latent compression framework for efficient multi-agent latent collaboration. LatCom maps multiple sender latents into a fixed number of receiver-readable and task-relevant slots. Rather than reconstructing all sender hidden states, it optimizes the compressed latents for receiver-side task utility. LatCom trains the compressor in two stages: single-sender readability learning first establishes a latent interface interpretable by the frozen receiver, and multi-sender fusion learning then trains the compressor to fuse complementary evidence and remove redundancy across agents. Experiments on multiple benchmarks with the Qwen-4B model show that LatCom improves average inference speed by 2.46x over LatentMAS, reduces token usage by 70.3%, and increases average accuracy by 0.46%.
%U https://aclanthology.org/2026.emnlp-main.1068/
%P 24531-24547
Markdown (Informal)
[LatCom: Cross-Agent Latent Compression for Efficient Multi-Agent Collaboration](https://aclanthology.org/2026.emnlp-main.1068/) (Zhang et al., EMNLP 2026)
ACL
- Shinan Zhang, Tao Zhang, Qihui Zhu, Mengjie Zhang, Dong Jin, Yunpeng Hou, Shuangwu Chen, Xiaobin Tan, Quan Zheng, and Jianyang. 2026. LatCom: Cross-Agent Latent Compression for Efficient Multi-Agent Collaboration. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 24531–24547, Budapest, Hungary. Association for Computational Linguistics.