CollabFlow: Recursive Self-Improvement of Agent Collaboration
Recursive self-improvement (RSI) lets a system improve from its own outcomes; in LLM-based multi-agent systems, Agents refine one another within a task, and outcomes improve how they collaborate across tasks. However, existing multi-agent collaboration leaves this loop open: collaboration is pre-defined at the operator level, topology-only learning keeps verbatim exchange that propagates errors, and reward maximization on a system's own outcomes concentrates on a few teams. To address these challenges, we propose CollabFlow, an RSI system of Learned Agent Collaboration: a trainable Collab-Director constructs teams of complete Agents, a frozen executor runs them, and each round's outcomes ...
| Authors | Xiao Huang, Mingda Zhang, Junming Zhang et al. |
|---|---|
| Primary category | cs.MA |
| Published | 29 September 2026 |
| Last updated at source | 29 September 2026 |
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