In-Context Black-Box Optimization with Unreliable Feedback

• URL: http://arxiv.org/abs/2605.06187v1
• Date: Thu, 07 May 2026 13:03:19 GMT
• Title: In-Context Black-Box Optimization with Unreliable Feedback
• Abstract summary: We study feedback-informed incontext black-box optimization (FICBO)<n>We introduce a structured feedback prior that models how feedback sources vary in their access, relevance, and distortion relative to the true objective, and use it to pretrain a feedback transformer.<n>On synthetic and real-world tasks, FICBO effectively exploits informative feedback while remaining robust to weak or misleading sources.
• Score: 20.948688720498644
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Black-box optimization in science and engineering often comes with side information: experts, simulators, pretrained predictors, or heuristics can suggest which candidates look promising. This information can accelerate search, but it can also be biased, input-dependent, or misleading. Feedback-aware BO methods typically handle one task at a time, limiting their ability to generalize over multiple sources of feedback. In-context optimizers address cross-task adaptation, but usually assume that optimization history is the only available signal 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 a structured feedback prior that models how feedback sources vary in their access, relevance, and distortion relative to the true objective, and use it to pretrain a feedback-aware transformer. 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, improving over other baselines. Empirical investigations further illustrate how the model perceives test-time sources, offering insights into its interpretability and decision-making process.

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