Reflection in the Dark: Exposing and Escaping the Black Box in Reflective Prompt Optimization
Shiyan Liu, Qifeng Xia, Qiyun Xia, Yisheng Liu, Xinyu Yu, Rui Qu

TL;DR
This paper introduces VISTA, a multi-agent framework for transparent and effective prompt optimization in large language models, overcoming black-box limitations and improving accuracy on benchmark datasets.
Contribution
VISTA decouples hypothesis generation from prompt rewriting, enabling interpretability and parallel verification, with a novel explore-exploit strategy to avoid local optima.
Findings
VISTA recovers accuracy to 87.57% on defective seed GSM8K.
VISTA outperforms baselines across GSM8K and AIME2025.
Identifies limitations of existing reflective APO methods.
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. We identify and empirically demonstrate four limitations: on GSM8K with a defective seed, GEPA degrades accuracy from 23.81% to 13.50%. We propose VISTA, a multi-agent APO framework that decouples hypothesis generation from prompt rewriting, enabling semantically labeled hypotheses, parallel minibatch verification, and interpretable optimization trace. A two-layer explore-exploit mechanism combining random restart and epsilon-greedy sampling further escapes local optima. VISTA recovers accuracy to 87.57% on the same defective seed and…
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Taxonomy
TopicsSoftware System Performance and Reliability · Software Testing and Debugging Techniques · Adversarial Robustness in Machine Learning
