Optstop: Adaptive Bayesian Framework Cuts LLM Evaluation Compute by Up to 97%

A new framework called optstop, introduced in a paper on arXiv (2608.14425), aims to reduce the computational cost of evaluating large language models (LLMs) by using a precision-based adaptive stopping rule. Traditional LLM evaluations typically allocate a fixed sampling budget, testing each benchmark item the same number of times even after performance estimates have become precise. Optstop treats evaluation as a sequential measurement problem: it continues sampling items where uncertainty is high and stops when estimates are precise or stable enough. The framework is built on hierarchical Bayesian inference and supports binary, ordinal, and continuous outcomes. It keeps every benchmark item eligible for sampling and does not require a calibrated item bank. Optstop can be applied live or retrospectively and includes a safeguard that increases caution as measured performance approaches zero, where rare successes are most informative. In an illustrative evaluation with 200 items and 10 epochs, optstop removed between 57% and 97% of planned trials across nine validation settings, while producing overall conclusions equivalent to the full run. These results suggest that LLM evaluation compute can be significantly reduced without compromising accuracy. The paper is authored by researchers and was announced on arXiv with the identifier 2608.14425v1.