The study's universal dw7 control net (b7c106nbt, 300sh/30k). Weights-only.

PyTorch checkpoint: {'model': state_dict, 'model_config': dict, 'spatial_subset': [...], 'global_subset': [...], 'step': int} (full checkpoints also carry optimizer). Inputs are a 14-channel subset of KataGo v7 spatial features + 2 global features; the engine, training pipeline, and evaluation harness will be released at https://github.com/sanderland/vibego (the study writeup lives there under experiments/WRITEUP.md). Strength numbers are judge scoreLead / win-rate Elo from paired color-reversed-opening matches at 48 visits/move with a kata1-b18 judge.

Distilled from the public kata1-b18c384nbt net over katagoarchive.org positions — credit to lightvector and the KataGo distributed-training contributors.