automatic-prompt-optimization

提出了 Reflective Feedback-Driven Prompt Optimization (RFDPO)，通过追踪 item 级别的恢复、回退和波动来评估自动提示优化，发现整体准确率可能掩盖大量变化，建议同时报告恢复、回归、churn 和未见数据上的表现。

Researchers introduce a chess-puzzle benchmark (1,118 Lichess puzzles) for automatic prompt optimization of frozen LLMs, evaluating six APO algorithms on eight models with cheap, deterministic, renewable scoring at a total cost of around $800.