{"ID":5663362,"CreatedAt":"2026-07-02T21:49:13.561239862Z","UpdatedAt":"2026-07-08T16:08:56.472507517Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.28661","arxiv_id":"2606.28661","title":"When More Sampling Hurts: The Modal Ceiling and Correlation Ceiling of Test-Time Scaling","abstract":"People overthink; language models over-sample, and the extra effort can talk both into a worse answer. Reasoning systems answer a hard question by sampling it many times (test-time scaling), and the more they draw, the more often a correct answer turns up somewhere, so coverage, the fraction of problems with at least one correct try, climbs and appears to be progress. But a deployed system must return one answer, and choosing it, not knowing which try is right, is selection; selection is capped, and past a point extra samples only make the model surer of a confident mistake, even as every draw adds cost. The gap between climbing coverage and stalled selection, the identifiability gap, is the answer a model can produce but not pick. So the real question is not whether to sample but how far, and the answer is: not far. For picking an answer, the vote has already settled within a few dozen draws, the modal ceiling; for scoring a benchmark, sooner still, the correlation ceiling. Beyond that, extra draws cost compute and add nothing, and can even make the answer worse. This paper turns the cutoff into a single number, the effective number of samples, that any sampling run already reveals. The bottleneck is recognizing a right answer, not generating one.","short_abstract":"People overthink; language models over-sample, and the extra effort can talk both into a worse answer. Reasoning systems answer a hard question by sampling it many times (test-time scaling), and the more they draw, the more often a correct answer turns up somewhere, so coverage, the fraction of problems with at least o...","url_abs":"https://arxiv.org/abs/2606.28661","url_pdf":"https://arxiv.org/pdf/2606.28661v1","authors":"[\"Yong Yi Bay\",\"Kathleen A. Yearick\"]","published":"2026-06-27T00:37:33Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"cs.CL\",\"stat.ML\"]","methods":"[\"Language Model\"]","has_code":false}