Bench to the Future: A Pastcasting Benchmark for Forecasting Agents

FutureSearch; :; Jack Wildman; Nikos I. Bosse; Daniel Hnyk; Peter Mühlbacher; Finn Hambly; Jon Evans; Dan Schwarz; Lawrence Phillips

arXiv · 2506.21558

Bench to the Future: A Pastcasting Benchmark for Forecasting Agents

FutureSearch·:·Jack Wildman·Nikos I. Bosse·Daniel Hnyk·Peter Mühlbacher·Finn Hambly·Jon Evans·Dan Schwarz·Lawrence Phillips

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Abstract

Forecasting is a challenging task that offers a clearly measurable way to study AI systems. Forecasting requires a large amount of research on the internet, and evaluations require time for events to happen, making the development of forecasting benchmarks challenging. To date, no forecasting benchmark provides a realistic, hermetic, and repeatable environment for LLM forecasters. We introduce Bench To the Future (BTF), a "pastcasting" benchmark with hundreds of high-quality questions for which the resolution is already known. Each question is accompanied by a large offline corpus of tens of thousands of relevant web pages, enabling a way to elicit realistic "forecasts" on past events from LLMs. Results suggest that our pastcasting environment can produce results comparable to those based on forecasts using the internet on at-the-time unresolved questions. We show results benchmarking agent and chain-of-thought forecasting approaches using several LLMs, including the recently-released Claude 4 models, and demonstrate BTF's ability to track steady forecasting capability progress over time. We intend this to be a living benchmark, with new questions added continually to account for increasing training data cutoff dates. We invite researchers to contact us at hello@futuresearch.ai to utilize our benchmark or tooling for their own research.

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FutureSearch, :, Jack Wildman, Nikos I. Bosse, Daniel Hnyk, Peter Mühlbacher, Finn Hambly, Jon Evans, Dan Schwarz, Lawrence Phillips. 2025-06-11. Bench to the Future: A Pastcasting Benchmark for Forecasting Agents. https://arxiv.org/abs/2506.21558

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