Weak-Link Optimization for Multi-Agent Reasoning and Collaboration
Summary: arXiv:2604.15972v1 Announce Type: new
Abstract: 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 weak-link optimization framework for multi-agent reasoning and collaboration, grounded in the weak-link principle.
Introduction to WORC
WORC follows a two-stage workflow designed to enhance the overall performance of multi-agent systems. The process begins with identifying agents that may be limiting the effectiveness of the group through their performance. By focusing on these weak links, WORC enables a more stable and efficient collaborative framework.
Two-Stage Workflow
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Weak Agent Localization Stage:
In this initial phase, task features are constructed, and a meta-learning-based weight predictor is utilized. This predictor is trained on optimal configurations identified by swarm intelligence algorithms (SIAs), enabling zero-shot mapping from task features to agent performance weights. The agent with the lowest predicted weight is subsequently identified as the weak agent.
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Weak-Link Optimization Stage:
In the second stage, an uncertainty-driven allocation strategy is employed. This strategy assigns additional reasoning budgets to the weak agents, allowing for larger repeated-sampling quotas based on their predicted weights. The idea is that lower predicted weights lead to larger allocations, compensating for their reliability deficiencies.
Experimental Results
Experimental results demonstrate that WORC achieves an impressive average accuracy of 82.2% on various reasoning benchmarks. Beyond just accuracy, WORC enhances framework stability and cross-architecture generalization. The findings suggest that by compensating for weak links rather than solely reinforcing strengths, the robustness of multi-agent systems can be significantly improved.
Conclusion
In conclusion, the WORC framework provides an innovative approach to addressing the challenges of multi-agent reasoning and collaboration. By focusing on weak agents and applying targeted strategies to optimize their performance, WORC presents a more holistic method for enhancing the effectiveness of multi-agent systems. Future research may explore the broader implications of weak-link optimization across different domains, potentially transforming how collaborative AI frameworks operate.
References
- arXiv:2604.15972v1