YarrowLab

Post-training research on bounded deviation from a statistical prior for event forecasting.

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.

Repositories

• 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.

More will be added here as real training runs land.