AlphaBet is a reinforcement learning framework that trains a single large language model to search for evidence, reason about it, and report probabilities for unresolved events across multiple pre-resolution dates. By scoring each dated forecast with a proper scoring rule, AlphaBet uses one resolved event to supervise decisions under varying information states, decomposing forecasting regret into evidence-acquisition and probability-reporting gaps. Experiments on GLM-4.5-Air show that AlphaBet improves Brier scores on Forecast-Dojo and FutureX, attributing gains to multi-date training and better information acquisition and probability reporting.
By Liqin Ye, Rongzhi Zhang, Xin Liu, Zheng Li, Yuan He, Qingyu Yin, Priyanka Nigam, Chao Zhang
The paper argues that traditional weather forecast evaluations, which focus on statistical comparisons between forecasts and observations, do not adequately capture how forecasts influence real-world decisions. It introduces decision calibration, a framework that assesses probabilistic forecast performance from the decision-maker’s perspective. Using this framework, the authors compare a machine learning model to a classical numerical weather prediction model across various weather-dependent decision tasks, finding that forecast-level performance does not reliably predict decision-level outcomes and that model rankings can shift depending on the decision context.
By Kornelius Raeth, Nicole Ludwig
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.
By Sadanand Singh, Allam Reddy, Manan Chopra
arXiv:2606. 15917v1 Announce Type: new Abstract: We use Group Relative Policy Optimization (GRPO), a recently devised sample and memory efficient reinforcement learning method, to finetune pretrained LLMs in the range of 1.
By Amit Arnold Levy
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.
By Rabanus Derr, Robert C. Williamson
Forecast-Dojo is a replayable environment designed to benchmark and train large language model (LLM) forecasting agents. It integrates resolved prediction‑market questions with dated news, enabling agents to research events and revisit predictions at successive historical dates. The platform includes 1,568 Polymarket events, 18.8 million dated news articles, and supports repeated evaluation, training interactions, and outcome feedback, with evidence that research tools lower Brier scores across 12 tested models, though all models still lag behind historical market forecasts.
By Liqin Ye, Haorui Wang, Fardin Ahmed, Rongzhi Zhang, Yuan He, Ziyuan Lin, Yanbin Yin, Jing Peng, Michael Galarnyk, Sudheer Chava, Chao Zhang
arXiv:2609.39386v1 Announce Type: new
Abstract: Pretrained time-series foundation models (TSFMs) are evaluated as forecasters of future values, yet for sparse series many decisions depend only on whi...
By Daniel Schoess, Florian von Wangenheim
The paper introduces World Reward Models (WRMs), a new approach for machine‑learning agents that selects the best candidate program directly from execution results instead of predicting intermediate outcomes. WRMs, exemplified by the ForeCritic model, are trained on thousands of executed program pairs and achieve a 79.4% success rate in choosing the better program, outperforming general‑purpose LLM judges. When integrated into systems like AIDE, ML‑Master, and ForeAgent, ForeCritic reduces mean task time by 4.4–7.5× while maintaining or improving overall performance.
By Yuxiao Lee, Zherui Yang, Fan Liu, Hao Liu
Vendster is an evaluation arena that studies how the bias and prediction‑interval width of time‑series forecasters influence the outcomes of buyer‑vendor discount negotiations conducted by large language model (LLM) agents. In its reference instance, Vendster‑M5, forecasts generated by LLMs increased sales target attainment by up to 17 percentage points, yet traditional forecast accuracy metrics did not predict negotiation success. Controlled experiments revealed that under‑prediction bias significantly improves outcomes for agents that react strongly to forecasts, while interval width has no notable effect when bias is zero.
By Charin Polpanumas
arXiv:2606. 29661v1 Announce Type: new Abstract: Top AI forecasting systems are approaching superforecaster-level accuracy on future world events, but still rely primarily on off-the-shelf LLMs combined with forecasting-specific context gathering and scaffolding.
By Matthew Aitchison, Scott Jeen, Toby Shevlane, Ben Day
arXiv:2610.33410v1 Announce Type: new
Abstract: Time-series tasks require both accurate predictions and fast decisions. Advances in large models have introduced decision-oriented models that directly...
By Jianye Wang, Kexin Zhang, Jiayin Xiong, Hao Xue
Top AI forecasting systems are approaching superforecaster-level accuracy on future world events, but still rely primarily on off-the-shelf LLMs combined with forecasting-specific context gathering and scaffolding. We study how to improve this recipe through ensembling: given a fixed number of samples, which off-the-shelf model forecasts should be combined to maximize accuracy?