Weak-Link Optimization for Multi-Agent Reasoning and Collaboration
LLM-driven multi-agent frameworks address complex reasoning tasks through multi-role collaboration. However, existing approaches often suffer from reasoning instability, where individual agent errors are amplified through collaboration, undermining overall performance. Current research mainly focuses on enhancing high-capability agents or suppressing unreliable outputs to improve framework effectiveness, while systematic identification and reinforcement of performance-limiting agents receive less attention. To address this gap, we propose WORC, a \underline{w}eak-link \underline{o}ptimization framework for multi-agent \underline{r}easoning and \underline{c}ollaboration, grounded in the we...
| Authors | Haoyu Bian, Chaoning Zhang, Jiaquan Zhang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 17 April 2026 |
| Last updated at source | 17 April 2026 |
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