Steering LLM Weights Boosts Divergent Thinking and Cuts Hive‑Mind Collapse
CreativityNeuro adjusts model weights without data, raising Divergent Association Task scores by up to 14 percentile points and improving originality in human‑rated Alternative Uses tests. The approach also lowers mode‑collapse metrics, showing a simple path to more creative, less homogenized LLM output.