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This arXiv preprint proposes two methods for making LLM multi-agent systems cheaper to run: SelectKV, which selectively shares attention cache states between agents, and HyLaMAS, which extends latent sharing to hybrid linear-attention models. The reported gains are a roughly 10% reduction in communicated KV payload with small accuracy costs (up to six points on HumanEval+), and 1.16-2.78x speedups with 53-80% fewer decoded tokens on Qwen3.5 models. Readers should note these are self-reported benchmark results on one model family, not yet validated in production multi-agent deployments.
Selective and Hybrid-State Communication for Latent Multi-Agent Systems
arXiv:2610.37779v1 Announce Type: cross Abstract: Efficient communication remains a major bottleneck in large language model multiagent systems. Latent communication reduces repeated natural-language exchange by transferring internal model states between agents, but these transfers remain costly and are typically designed around conventional KV-cache representations. We address this problem from two complementary perspectives. We introduce SelectKV, a boundary-aware method that selectively communicates KV states while protecting inherited context, and HyLaMAS, which extends latent collaboration to hybrid linear-attention models by communicating both KV caches and recurrent states through a shared workspace. Across six benchmarks, SelectKV reduces communicated KV positions and logical KV payload by 9.7-9.9%, with accuracy unchanged on two benchmarks, within one percentage point on three, and six points lower on HumanEval+. Across Qwen3.5 model scales, HyLaMAS reduces decoded output tokens by 53-80% and achieves 1.16-2.78x speedups relative to text-based multi-agent communication. Together, these independently evaluated methods represent two complementary approaches to efficient latent-state communication across conventional Transformer and hybrid model architectures.
Abstract:Efficient communication remains a major bottleneck in large language model multiagent systems. Latent communication reduces repeated natural-language exchange by transferring internal model states between agents, but these transfers remain costly and are typically designed around conventional KV-cache representations. We address this problem from two complementary perspectives. We introduce SelectKV, a boundary-aware method that selectively communicates KV states while protecting inherited context, and HyLaMAS, which extends latent collaboration to hybrid linear-attention models by communicating both KV caches and recurrent states through a shared workspace. Across six benchmarks, SelectKV reduces communicated KV positions and logical KV payload by 9.7-9.9%, with accuracy unchanged on two benchmarks, within one percentage point on three, and six points lower on HumanEval+. Across Qwen3.5 model scales, HyLaMAS reduces decoded output tokens by 53-80% and achieves 1.16-2.78x speedups relative to text-based multi-agent communication. Together, these independently evaluated methods represent two complementary approaches to efficient latent-state communication across conventional Transformer and hybrid model architectures.
Multiagent Systems (cs.MA); Machine Learning (cs.LG)
arXiv:2610.37779 [cs.MA]
(or arXiv:2610.37779v1 [cs.MA] for this version)
https://doi.org/10.48550/arXiv.2610.37779 Focus to learn more arXiv-issued DOI via DataCite
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