FutureSim: Replaying World Events to Evaluate Adaptive Agents

Shashwat Goel, Nikhil Chandak, Arvindh Arun, Ameya Prabhu, Steffen Staab, Moritz Hardt, Maksym Andriushchenko, Jonas Geiping

Keywords: Language Models, Agents, Prediction, Forecasting, Search, World Models

TL;DR: We build a forecasting simulation grounded in real-world events to study how AI agents adapt their predictions based on new information arriving at each time-step

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 calibration than a constant predictor. 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 long-horizon open-ended adaptation spanning multiple months in the real world.

Submission Number: 124