The method uses the full distribution of scoring-token logits to estimate continuous rewards and evaluation uncertainty without additional training. It scales verification through finer score granularity, repeated evaluations, and decomposition of evaluation criteria, then uses Probabilistic Pivot Tournaments to select strong candidates under a limited budget.
The framework supports test-time scaling, progress tracking, reinforcement learning, and agent benchmarking. It is available as installable tooling with public code and documentation, making it useful for teams that need more informative feedback than pass-or-fail judging.
