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 Qwen3-4B show that LatCom achieves an average 2.46x inference speed-up over LatentMAS and reduces output token usage by 70.3% while maintaining comparable average accuracy.
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- Open access
- Green open access
Cite this article
APA 7
Zhang, S., Zhang, T., Zhu, Q., Zhang, M., Jin, D., Hou, Y., Chen, S., Tan, X., Zheng, Q., & Yang, J. (2026). LatCom: Cross-Agent Latent Compression for Efficient Multi-Agent Collaboration. https://omanscience.com/en/articles/latcom-cross-agent-latent-compression-for-efficient-multi-agent-collaboration
MLA 9
Zhang, Shinan, et al. "LatCom: Cross-Agent Latent Compression for Efficient Multi-Agent Collaboration." https://omanscience.com/en/articles/latcom-cross-agent-latent-compression-for-efficient-multi-agent-collaboration.
Chicago (author–date)
Zhang, Shinan, Tao Zhang, Qihui Zhu, Mengjie Zhang, Dong Jin, Yunpeng Hou, Shuangwu Chen, Xiaobin Tan, Quan Zheng, and Jian Yang. 2026. "LatCom: Cross-Agent Latent Compression for Efficient Multi-Agent Collaboration." https://omanscience.com/en/articles/latcom-cross-agent-latent-compression-for-efficient-multi-agent-collaboration.
Harvard
Zhang, S., Zhang, T., Zhu, Q., Zhang, M., Jin, D., Hou, Y., Chen, S., Tan, X., Zheng, Q. and Yang, J. (2026) 'LatCom: Cross-Agent Latent Compression for Efficient Multi-Agent Collaboration', Available at: https://omanscience.com/en/articles/latcom-cross-agent-latent-compression-for-efficient-multi-agent-collaboration.
Vancouver
Zhang S, Zhang T, Zhu Q, Zhang M, Jin D, Hou Y, et al. LatCom: Cross-Agent Latent Compression for Efficient Multi-Agent Collaboration. https://omanscience.com/en/articles/latcom-cross-agent-latent-compression-for-efficient-multi-agent-collaboration
IEEE
S. Zhang, T. Zhang, Q. Zhu, M. Zhang, D. Jin, Y. Hou, S. Chen, X. Tan, Q. Zheng, and J. Yang, "LatCom: Cross-Agent Latent Compression for Efficient Multi-Agent Collaboration," https://omanscience.com/en/articles/latcom-cross-agent-latent-compression-for-efficient-multi-agent-collaboration.