Budget-Aware LLM Discovery via Cost-Calibrated Frontier Utility2607.26828AuthorsYansen Zhang,Yilu Liu,Xiaokun Zhang,Xue Liu,Yiyan Qiand 4 moreAbstractLarge language models increasingly support scientific and algorithmic discovery through inference-time search over evaluated candidates. Existing adaptive discovery controllers assign credit based only on score progress, even though prompt length, retries, and guidance calls cause search actions to incur different token costs. We prove that cost-blind credit can forfeit all but a vanishing fraction of attainable quality as frontiers multiply and costs diverge. Under a fixed search-side token budget, the controller must decide which frontier is improving and whether its gain justifies the realized cost before the budget is exhausted. We introduce CostAda, a cost-calibrated adaptive controller built around cost-calibrated frontier utility. The utility values frontier progress relative to realized action cost and conditions that credit on the remaining budget. CostAda uses this signal to control local exploration intensity, frontier allocation, and budgeted tactic intervention. Cost and remaining budget therefore shape the search rather than serving only as accounting variables or a stopping rule. CostAda reaches the strongest baseline's full-budget quality with at most half the budget on twelve of sixteen benchmark--backbone pairs while achieving the strongest mean final quality on all eight benchmarks under GLM-5 and GPT-5.4.ResourcesView on Hugging FaceRead PDFArXiv
Budget-Aware LLM Discovery via Cost-Calibrated Frontier Utility
Large language models increasingly support scientific and algorithmic discovery through inference-time search over evaluated candidates. Existing adaptive di...
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