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… See the full description on the dataset page: https://huggingface.co/datasets/neurarch-ai/arch-verifier-grounding-264.