YarrowLab
Research and models for event forecasting.
Models
yarrow-forecast— forecasts U.S. party primary and nomination outcomes. Given a question, its resolution criteria, and the evidence available as of a stated date, one forward pass returns a calibrated probability in [0, 1]. Out of sample on 150 settled questions across 33 election nights, Brier score is 0.175–0.185, against 0.239 for the historical base rate. CC BY-NC-4.0.
Research
Post-training work on bounded deviation from a statistical prior.
Instead of asking a model to output a probability directly, we train it to
output Δ — a bounded deviation from a prior p₀ computed upstream from
reference-class statistics — plus an attribution category for why the
prior might be wrong, and an evidence span supporting that attribution.
The final probability p = σ(logit(p₀) + Δ) is composed in code; the model
never sees or touches market prices.
yarrow-delta-sft-v1-smoke— a pipeline-validation checkpoint (LoRA over a randomly initialized tiny model). Confirms the SFT loop runs end to end; not a trained model.