This is one of the more concrete decision-layer writeups I've seen -- the fixed Choice/Score/Noul shapes are the right idea for cutting hallucination risk on format. One thing I'd want nailed down before trusting it for a money-movement gate though: format safety isn't calibration safety. A Score or Noul call can return a confident, well-formed 0.95 that's still wrong, and the piece only treats a timeout as a failure worth an explicit fallback. I'd want a low-confidence or out-of-distribution case handled the same way -- explicit "not checked," not silently trusted just because it parsed cleanly. Narrow classifiers gating write actions tend to fail quietly like that: they don't stop returning a shape, they just drift as the input distribution shifts, and nothing distinguishes "confident and right" from "confident and wrong" unless someone's actively watching calibration after launch, not just during the initial shadow-mode switch-on.
This is one of the more concrete decision-layer writeups I've seen -- the fixed Choice/Score/Noul shapes are the right idea for cutting hallucination risk on format. One thing I'd want nailed down before trusting it for a money-movement gate though: format safety isn't calibration safety. A Score or Noul call can return a confident, well-formed 0.95 that's still wrong, and the piece only treats a timeout as a failure worth an explicit fallback. I'd want a low-confidence or out-of-distribution case handled the same way -- explicit "not checked," not silently trusted just because it parsed cleanly. Narrow classifiers gating write actions tend to fail quietly like that: they don't stop returning a shape, they just drift as the input distribution shifts, and nothing distinguishes "confident and right" from "confident and wrong" unless someone's actively watching calibration after launch, not just during the initial shadow-mode switch-on.