| # YarrowLab |
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| Research and models for event forecasting. |
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| ## Models |
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| - [`yarrow-forecast`](https://huggingface.co/YarrowLab/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](https://creativecommons.org/licenses/by-nc/4.0/). |
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| ## Research |
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| 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. |
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| - [`yarrow-delta-sft-v1-smoke`](https://huggingface.co/YarrowLab/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. |
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