Tag
This paper proposes WORC, a weak-link optimization framework for multi-agent LLM systems that identifies and reinforces underperforming agents through meta-learning-based weight prediction and uncertainty-driven resource allocation, achieving 82.2% accuracy on reasoning benchmarks while improving system stability.