New WORC framework identifies and strengthens weak-performing agents in multi-agent AI systems to prevent error amplification during collaborative reasoning

WORC uses weak-link optimization principle to systematically identify performance-limiting agents rather than just boosting high-capability ones

Two-stage approach combines task feature construction with meta-learning-based weight prediction trained on swarm intelligence algorithms

Addresses reasoning instability problem where individual agent errors cascade and amplify through multi-role collaboration in LLM frameworks

Focuses on reinforcement of underperforming agents as overlooked strategy to improve overall multi-agent framework effectiveness