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.