Decoupled Multi-Agent Orchestration
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 from the pool, enabling conditional credit assignment to decomposition and collaboration decisions. A lightweight matcher estimates worker suitabili...
| Authors | Xinle Wu, Yao Lu |
|---|---|
| Primary category | cs.AI |
| Published | 6 October 2026 |
| Last updated at source | 7 October 2026 |
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