AI RESEARCH
Uncertainty-Aware Budget Allocation for Adaptive Test-Time Reasoning
arXiv CS.CL
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ArXi:2605.26849v1 Announce Type: new Sampling multiple responses improves language model reasoning, but uniform compute allocation is inefficient: easy questions are over-sampled while hard questions remain under-explored. We propose Uncertainty-Aware Budget Allocation (UAB), a concave integer optimization framework that reallocates a fixed sampling budget based on per-question uncertainty estimated at no additional inference cost.