automated-prompt-engineering

CANTANTE introduces a contrastive credit attribution method to optimize multi-agent LLM systems by decomposing global rewards into per-agent signals, enabling automated prompt tuning. It outperforms baselines on programming, math, and retrieval benchmarks, achieving up to +18.9 points improvement without increased inference cost.