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

LLM-based multi-agent systems have nstrated strong performance across complex real-world tasks, such as software engineering, predictive modeling, and retrieval-augmented generation. Yet, automating their configuration remains a structural challenge. Researchers are often forced into manual, trial-and-error prompt tuning, where a change to a single agent shifts the global output in ways that are difficult to trace. The core bottleneck is credit assignment: while the parameters governing agent behavior are local, performance scores are only available at the global system level.