Reflection in the Dark: Exposing and Escaping the Black Box in Reflective Prompt Optimization 事件

Reflection in the Dark: Exposing and Escaping the Black Box in Reflective Prompt Optimization arXiv:2603.18388v2 Announce Type: replace Abstract: Automatic prompt optimization (APO) has emerged as a powerful paradigm for improving LLM performance without manual prompt engineering. Reflective APO methods such as GEPA iteratively refine prompts by diagnosing failure cases, but the optimization process remains black-box and label-free, leading to uninterpretable trajectories and systematic failure