Learning Latency-Aware Orchestration for Multi-Agent Systems
Quick summary
arXiv:2601.10560v2 Announce Type: replace-cross Abstract: Multi-agent systems (MAS) coordinate multiple LLM-powered agents through structured workflows, gaining reasoning power but incurring high inference latency from multi-step execution and repeated model invocations. Existing orchestration methods primarily optimize task performance and inference cost, leaving latency largely unaddressed. In MAS, end-to-end latency is governed by the \textit{critical execution path}, so reducing total cost alone does not reliably reduce latency. Moreover, optimizing latency while preserving accuracy remain
Key takeaways
- arXiv:2601.10560v2 Announce Type: replace-cross Abstract: Multi-agent systems (MAS) coordinate multiple LLM-powered agents through structured workflows, gaining reasoning power but incurring high inference latency from multi-step execution and repeated model invocations.
- Existing orchestration methods primarily optimize task performance and inference cost, leaving latency largely unaddressed.
- In MAS, end-to-end latency is governed by the \textit{critical execution path}, so reducing total cost alone does not reliably reduce latency.
Why it matters
This model development creates a new option for users and a new testing obligation for developers. A fixed evaluation set comparing quality, cost and failure behavior is more useful than launch claims.
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