FICBO：利用不可靠反馈的上下文黑盒优化方法
In-Context Black-Box Optimization with Unreliable Feedback
AI 导读
研究者提出反馈感知的上下文黑盒优化方法 FICBO，让预训练的 feedback-aware transformer 在决策时同时利用优化历史和候选集的廉价辅助反馈。该方法通过结构化先验在从高信息量到误导性的多种反馈模式下进行预训练，测试时通过对比观测目标值与辅助信号来估计反馈源可靠性。在合成与真实任务上，FICBO 能有效利用有用反馈，同时对微弱或误导性反馈源保持稳健。
正文 · 原文
Abstract:Black-box optimization in science and engineering often comes with side information: experts, simulators, pretrained predictors, or heuristics can suggest promising candidates. Such feedback can accelerate search, but can also be biased, input-dependent, or misleading. Feedback-aware Bayesian optimization methods typically handle tasks independently, limiting adaptation across feedback sources. In-context optimizers enable cross-task adaptation, but usually rely only on optimization history at test time. We study feedback-informed in-context black-box optimization (FICBO), where a pretrained optimizer conditions on both the observed history and cheap auxiliary feedback for the current candidate set. We introduce structured priors over how auxiliary feedback relates to the objective, and use them to pretrain a feedback-aware transformer across regimes ranging from highly informative to misleading. At test time, the model estimates source reliability in context by comparing observed objective values with auxiliary signals, improving query selection. On synthetic and real-world tasks, FICBO effectively exploits informative feedback while remaining robust to weak or misleading sources. Empirical investigations further illustrate how the model perceives test-time sources, offering insights into its interpretability and decision-making process.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.06187 [cs.LG] |
| (or arXiv:2605.06187v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.06187 arXiv-issued DOI via DataCite |
Submission history
From: Nicolas Blumer [view email]
[v1]
Thu, 7 May 2026 13:03:19 UTC (1,425 KB)
[v2]
Wed, 4 Nov 2026 11:56:06 UTC (1,977 KB)
来源：arXiv cs.AI · arxiv.org