Decoupled Multi-Agent Orchestration
Quick summary
arXiv:2610.07556v1 Announce Type: new Abstract: Learned orchestration can automatically construct effective language-model multi-agent systems, but existing approaches couple planning to fixed worker pools and train decomposition and collaboration from the same terminal outcome, limiting transfer and obscuring credit assignment. We introduce DeOrch, which separates worker-agnostic planning from concrete worker selection. Its two-stage planner first decomposes the task without worker information, then chooses collaboration operations using compact, worker-identity-free matchability feedback fro
Key takeaways
- arXiv:2610.07556v1 Announce Type: new Abstract: Learned orchestration can automatically construct effective language-model multi-agent systems, but existing approaches couple planning to fixed worker pools and train decomposition and collaboration from the same terminal outcome, limiting transfer and obscuring credit assignment.
- We introduce DeOrch, which separates worker-agnostic planning from concrete worker selection.
- Its two-stage planner first decomposes the task without worker information, then chooses collaboration operations using compact, worker-identity-free matchability feedback fro
Why it matters
“Decoupled Multi-Agent Orchestration” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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