BayesBench slashes LLM benchmarking compute costs

Traditional brute-force benchmarking is a "carbon-for-confidence" trap that prioritizes sample volume over statistical efficiency. BayesBench addresses this by enabling early stopping in evaluation runs, saving compute by terminating once statistical significance is achieved. It moves beyond binary metrics to provide a continuous, posterior-based view of model capabilities while specifically targeting the high cost of evaluating agents that require complex interactions. A potential bottleneck exists in extracting clear signals when model performance differences are extremely subtle or noise levels are high.