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 early steps are more reliable than later ones, working with these reliable early steps instead of the f
The paper introduces a novel architecture, StreamMA, that addresses fundamental limitations in multi-agent reasoning, specifically regarding latency and effectiveness.
This innovation significantly improves the efficiency and reliability of multi-agent systems, enabling more complex and time-sensitive AI applications.
Traditional 'generate-then-transfer' paradigms in multi-agent systems are challenged by a new 'streaming' approach that reduces latency and enhances performance by leveraging early, reliable reasoning steps.
- · AI developers
- · Companies adopting multi-agent systems
- · Cloud computing providers
- · Robotics
- · Legacy multi-agent architectures
- · Systems heavily reliant on sequential processing
Reduced latency in multi-agent systems will accelerate the development and deployment of sophisticated AI applications.
Improved effectiveness by utilizing early, reliable reasoning steps could lead to more robust and trustworthy AI solutions.
The ability to pipeline agent reasoning efficiently may enable the creation of highly complex, real-time autonomous systems where previous architectures struggled.
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Read at arXiv cs.AI