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APEX introduces a dynamic data selection strategy for automatic prompt optimization, stratifying datasets into easy, hard, and mixed tiers to improve data efficiency, achieving significant performance gains over initial prompts on multiple benchmarks.
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.