Diversity is the Strength of the AI Crowd
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? On binary questions from the Metaculus AI Benchmark, we find that individual accuracy is not enough: many frontier LLMs make highly correlated predictions, limiting the value of additional forecasts from the same or similar models. Instead, the strongest ensembles combine accurate but diverse forecasters, with mode...
| Authors | Matthew Aitchison, Scott Jeen, Toby Shevlane et al. |
|---|---|
| Primary category | cs.AI |
| Published | 29 June 2026 |
| Last updated at source | 29 June 2026 |
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