Paper shows GPT-6 Astra can read and directly edit raw neural network weights
A paper, 'Sparks of Weight-Space Programming' (Takehi, Teoh, Zhang, Khattab), reports that GPT-6 Astra can infer a neural network's training objective purely from its raw weights, write small game-playing networks from scratch that outperform networks trained with PPO for 100 million steps, and raise a Qwen3.5-0.8B model's MATH-500 score from 7.9% to 48.1% (also roughly tripling IFEval) by directly editing 0.016% of its weights without running any training code. Simon Willison independently reproduced a related weight-interpretation task (identifying which simplified game a policy network was trained on) using the locally-run Qwen 3.8 27B and, via OpenRouter, Kimi K3.1 Pro; both succeeded. spaCy author Matthew Honnibal called the paper misleading for conflating two distinct capabilities, and engineer Sean Goedecke argued the Qwen3.5-0.8B fix example mirrors steps a human ML researcher would already take.