Two Calls, Two Moments, and the Vote-Accuracy Curve of Repeated LLM Inference
Repeated sampling can improve LLM accuracy, and quantifying the gains from additional calls is essential for allocating test-time compute. We study binary decisions, a fundamental setting where repeated answers to the same question are aggregated by majority vote. We show that two independently sampled responses per example in a validation set with known answers constrain the latent distribution of example-level success probabilities to a class consistent with the paired outcomes. Optimizing over this class yields sharp accuracy and gain bounds at every finite voting budget. A shared large-sample confidence region accounts for validation uncertainty. We also obtain sharp infinite-vote bou...
| Authors | Yi Liu |
|---|---|
| Primary category | cs.LG |
| Published | 5 May 2026 |
| Last updated at source | 3 October 2026 |
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