Streaming Communication in Multi-Agent Reasoning
Summary
StreamMA introduces a streaming communication paradigm for multi-agent reasoning that pipelines intermediate results to reduce latency and improve effectiveness by leveraging more reliable early steps, outperforming baselines across benchmarks and revealing a step-level scaling law.
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Paper page - Streaming Communication in Multi-Agent Reasoning
Source: https://huggingface.co/papers/2606.05158
Abstract
StreamMA enables efficient multi-agent reasoning by streaming intermediate results and leveraging reliable early steps to improve both latency and effectiveness across various reasoning tasks.
Multi-agent reasoning systemsadopt a “generate-then-transfer” paradigm that forcesend-to-end latencyto scale linearly with pipeline depth. We introduce StreamMA, a multi-agent reasoning system that streams each reasoning step to downstream agents as soon as it is generated,pipeliningadjacent agents and thus reducing latency. Surprisingly, thispipeliningalso improves effectiveness: because multi-step reasoning quality is non-uniform and early steps are more reliable than later ones, working with these reliable early steps instead of the fullchainprevents error-prone late steps from misleading downstream agents. We formalize both advantages with the first closed-form joint analysis of stream, serial, andsingle protocols, deriving theeffectiveness ordering,speedup upper bound, andcost ratio. Across eightreasoning benchmarksspanning mathematics, science, and code, two frontierLLMs(Claude Opus 4.6 and GPT-5.4), and three topologies (Chain,Tree,Graph), StreamMA outperforms both baselines (avg. +7.3 pp, max +22.4 pp on HMMT 2026; Claude Opus 4.6-high). Beyond these contributions, we discover a “step-level scaling law”: increasing per-agent steps consistently improves both effectiveness and efficiency, a new scaling dimension orthogonal to and composable withagent-count scaling.
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