StreamMA: Making Multi-Agent Systems Faster and More Accurate!
Hey everyone! Our team just released StreamMA. It is a new way to make multi-agent systems both faster and more accurate. The core idea is simple but powerful: Instead of waiting for one agent to finish its full response, we now send information step by step. Each reasoning step goes to the next agent right away.
Why does this work so well?
The upstream agent sends one step, and the downstream agents start working immediately. This makes everything much faster. Surprisingly, the reasoning quality also gets better. Complex reasoning steps are not all the same quality. Early steps are usually reliable and clear. Later steps are more likely to have errors. StreamMA lets downstream agents start with the good early steps. By the time any mistakes arrive, the other agents have already built their own strong reasoning path. This naturally reduces the impact of errors.
Key Contributions:
① Stream Protocol: We change communication from full responses to single steps. This improves both speed and performance.
② Three closed-form theorems: We show when Stream works best, the theoretical speedup limit, and the cost ratio.
③ Step-level scaling law: With the same number of agents, adding more steps improves both accuracy and speed. It works well together with traditional agent scaling.
Strong Experimental Results:
• Performance: Average +7.3 percentage points better on 8 math, science, and code benchmarks (Opus 4.6)
• Speedup: With 64 agents and 64 steps, we get 26.9× faster inference (theoretical max is 32.3×, we reached 83% of it) (HMMT26 GPT5.4)
Want to learn more?
Paper: https://huggingface.co/papers/2606.05158
Project Page: https://zhenyangcs.github.io/StreamMA-website
GitHub: https://github.com/EnVision-Research/StreamMA
If you work on multi-agent systems, distributed reasoning, or long-chain inference, we would love to hear your thoughts! This streaming idea brings new possibilities.
#StreamMA #MultiAgent #AI #Reasoning #LLM
