Streaming Communication in Multi-Agent Reasoning
📰 ArXiv cs.AI
Learn how StreamMA reduces latency in multi-agent reasoning systems by streaming each reasoning step to downstream agents, improving effectiveness and reducing end-to-end latency.
Action Steps
- Implement StreamMA to pipeline adjacent agents in a multi-agent reasoning system
- Stream each reasoning step to downstream agents as soon as it is generated
- Evaluate the effectiveness of StreamMA in reducing latency and improving quality
- Compare the performance of StreamMA with traditional generate-then-transfer paradigms
- Apply StreamMA to real-world multi-agent reasoning applications
Who Needs to Know This
Researchers and engineers working on multi-agent systems and reasoning pipelines can benefit from this knowledge to improve the efficiency and effectiveness of their systems.
Key Insight
💡 Pipelining adjacent agents in multi-agent reasoning systems can improve effectiveness and reduce latency
Share This
🚀 StreamMA reduces latency in multi-agent reasoning systems by streaming each step to downstream agents! 🤖
Full Article
Title: Streaming Communication in Multi-Agent Reasoning
Abstract:
arXiv:2606.05158v1 Announce Type: cross Abstract: Multi-agent reasoning systems adopt a "generate-then-transfer" paradigm that forces end-to-end latency to 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, pipelining adjacent agents and thus reducing latency. Surprisingly, this pipelining also improves effectiveness: because multi-step reasoning quality is non-uniform and ear
Abstract:
arXiv:2606.05158v1 Announce Type: cross Abstract: Multi-agent reasoning systems adopt a "generate-then-transfer" paradigm that forces end-to-end latency to 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, pipelining adjacent agents and thus reducing latency. Surprisingly, this pipelining also improves effectiveness: because multi-step reasoning quality is non-uniform and ear
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