arXiv:2401. 14483v4 Announce Type: replace Abstract: In the current practices of machine learning, the evaluation of forecasts has become a cornerstone of scientific progress.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.
arXiv:2605. 26703v2 Announce Type: replace-cross Abstract: The classic concept of "calibrated forecasts" and its more recent refinement, "calibeating," are defined with respect to the standard quadratic scoring rule.
arXiv:2607. 00164v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards can in principle train calibrated probabilistic forecasters, since a proper scoring rule such as the Brier score is computed from outcomes alone and is minimized in expectation by the true probability.
arXiv:2607. 06166v1 Announce Type: new Abstract: Prediction markets aggregate dispersed beliefs into prices that act as probabilistic forecasts of uncertain events.
arXiv:2607. 17765v1 Announce Type: cross Abstract: We introduce WC2026-Agents, a benchmark and dataset for evaluating large language models (LLMs) as autonomous forecasting agents on real, future events.
arXiv:2607. 03015v1 Announce Type: new Abstract: Forecasting future events has attracted growing attention as a testbed for general-purpose AI.
arXiv:2607. 10972v1 Announce Type: new Abstract: Many evaluations of model outputs rely either on contracts checkable at evaluation time or on feedback that arrives within the operating loop.