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7994c82 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | # 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`](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.
More will be added here as real training runs land. |