neurarch-ai/arch-verifier-grounding-264

Verifier grounding study (264 graphs)

Clean reference architectures plus systematically corrupted variants (broken attention head divisibility, linear width mismatches, severed connections), each built as a real PyTorch model and run on a GPU. Every row pairs the static verifier verdict with what actually happened at runtime: whether the module constructed, whether the forward pass survived, whether training made progress, and the initial and final loss. 264 graphs, two seeds, torch 2.8.

• —Size: 264 graphs
• —Licence: MIT License
• —Canonical page: https://neurarch.com/d/grounding-264.html
• —On the site: https://neurarch.com/caught-bugs.html

Fields

• —verifier score
• —verifier blocked
• —constructs
• —forward pass ok
• —makes training progress
• —initial loss
• —final loss

What this dataset is not

The corruptions are injected, not sampled from real mistakes, so the failure modes in here are the ones we chose to plant. This settles that a blocked graph does not run. It says nothing about how often an engineer writes one, and a paper quoting it as a base rate would be quoting it wrong.

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. Verifier grounding study (264 graphs). https://neurarch.com/d/grounding-264.html