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:2608. 09948v1 Announce Type: cross Abstract: No single AI weather model excels at all variables, pressure levels, and lead times.
By Qiang Wu, Han Li, Jianping Huang
arXiv:2607. 18269v1 Announce Type: new Abstract: The wisdom of crowds -- the finding that aggregating judgments across individuals often outperforms the best individual -- has been extensively studied with human forecasters.
By Igor Douven
arXiv:2608. 08954v1 Announce Type: cross Abstract: Ensemble forecasting aims to sample the conditional distribution of outcomes; whether AI forecast ensembles do this correctly in a joint sense remains largely untested.
By Lucas J. Howard, Elizabeth A. Barnes
arXiv:2608. 10149v1 Announce Type: new Abstract: Due to the diversity of real-world time series, no single forecasting model consistently dominates across all samples.
By Xu Zhang, Chang Xu, Hui Sun, Nan Ma, Zijian Zhang, Peng Wang, Wei Wang, Li Zhao
arXiv:2602. 13792v2 Announce Type: replace Abstract: Artificial intelligence built on large foundation models has transformed language understanding, computer vision, and reasoning, yet these systems remain isolated and cannot readily share their capabilities.
By Siyang Li, Chenhao Liu, Dongrui Wu, Zhigang Zeng, Lieyun Ding