CANTANTE: Optimizing Agentic Systems via Contrastive Credit Attribution [R]

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• CANTANTE introduces a novel approach to optimize LLM-based multi-agent systems by solving the credit assignment problem, which is currently challenging in existing methods.
• The key innovation of CANTANTE lies in its ability to decompose global rewards into per-agent update signals through a process involving local optimizers and an attributer. This allows for more autonomous and trustworthy agent configurations without requiring manual tuning.

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