#weight-space programming

Simon Willison reports that Qwen 3.8 27B, running locally in LM Studio from a 17GB Q4\K\M build, identified Pac-Man from a prompt containing raw policy-network weights, answering after 46m12s and 43,506 reasoning tokens with 72% stated confidence; he also reports it identified Pong. The prompt comes from &\x27;Sparks of Weight-Space Programming,&\x27; a paper-shaped blog post by Rikiya Takehi, Jayden Teoh, Alex Zhang, and Omar Khattab that claims frontier models, especially GPT-6 Astra, can interpret raw weights and, in one example, edit 0.016% of Qwen3.5-0.8B&\x27;s weights to raise MATH-500 from 7.9% to 48.1%. Those claims are contested: Matthew Honnibal called the paper misleading for conflating tool-free weight reading with code-assisted model updates, and Sean Goedecke argued the Qwen3.5-0.8B edit follows steps a human ML researcher would take. Willison&\x27;s laptop results are informal, individually reported runs of the game-identification experiment, not independent confirmation of the paper&\x27;s broader editing claims.