759-MFLOP tier champion (1.38M params, ~11.2 single-thread CPU-ms): first net of the study to decisively beat g170-b6c96 (+14.4 sL [2.5,26.3] / Elo +124 [40,227], 64 games, 48v). Full checkpoint.

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.