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Structure, verdict and trained outcome triples

The corpus that pairs what a design looks like with what it did. Each row carries a structural fingerprint of a graph (task family, parameter count, component count), the static verifier verdict on it (score and blockers), and the outcome of actually training it: the full loss curve, best and final loss, area under the loss curve, step count, and whether it converged. Every graph was trained through the same managed-GPU path the product uses, so the outcome is measured rather than simulated.

Fields

  • task family
  • parameter count
  • component count
  • verifier score
  • blockers
  • loss curve
  • best loss
  • final loss
  • steps
  • converged

What this dataset is not

n is 80, and the headline finding is negative: across families the correlation between verifier score and achievable quality is about zero (rho -0.02), undefined inside four families whose graphs score identically, and positive only where the score varies. It is published because that is the result. The verifier tells you whether a graph runs, not how good it will get, and this is the file that shows it.

Verify a design of your own

These rows describe neural network graphs that were checked by a deterministic verifier, and the same verifier is callable:

curl -X POST https://www.neurarch.com/api/v1/check \
  -H "Authorization: Bearer $NEURARCH_API_KEY" \
  -H "Content-Type: application/json" \
  -d @graph.json

Cite

Neurarch. Structure, verdict and trained outcome triples. https://neurarch.com/d/outcome-triples.html
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