| title | FutureSim: Replaying World Events to Evaluate Adaptive Agents |
|---|---|
| authors | Shashwat Goel, Nikhil Chandak, Arvindh Arun, Ameya Prabhu, Steffen Staab, Moritz Hardt, Maksym Andriushchenko, Jonas Geiping |
| date | 2026-05-14 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2605.15188 |
| affiliation | Max Planck Institute for Intelligent Systems |
| upvotes | 5 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | AI agents are being increasingly deployed in dynamic, open-ended environments that require adapting to new information as it arrives. To efficiently measure this capability for realistic use-cases, we propose building grounded simulations that replay real-world events in the order they occurred. We build FutureSim, where agents forecast world events beyond their knowledge cutoff while interacting with a chronological replay of the world: real news articles arriving and questions resolving over the simulated period. We evaluate frontier agents in their native harness, testing their ability to predict world events over a three-month period from January to March 2026. FutureSim reveals a clear separation in their capabilities, with the best agent's accuracy being 25%, and many having worse Brier skill score than making no prediction at all. Through careful ablations, we show how FutureSim offers a realistic setting to study emerging research directions like long-horizon test-time adaptation, search, memory, and reasoning about uncertainty. Overall, we hope our benchmark design paves the way to measure AI progress on open-ended adaptation spanning long time-horizons in the real world. |
cite
@misc{goel2026futuresim,
title={FutureSim: Replaying World Events to Evaluate Adaptive Agents},
author={Shashwat Goel and Nikhil Chandak and Arvindh Arun and Ameya Prabhu and Steffen Staab and Moritz Hardt and Maksym Andriushchenko and Jonas Geiping},
year={2026},
eprint={2605.15188},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2605.15188}
}