AI RESEARCH
In-Context Black-Box Optimization with Unreliable Feedback
arXiv CS.LG
•
ArXi:2605.06187v1 Announce Type: new 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.