LLMs Can Directly Read and Write Neural Network Weights: Astra Turns Qwen3.5-0.8B's…

A blog post by Rikiya Takehi, Jayden Teoh, and others proposes Weight-Space Programming, finding that frontier LLMs such as GPT-6 Astra can directly read and write neural network weights without gradient descent or training loops. Using only the raw weights, Astra can identify the game a network was trained on, the shapes generated by a diffusion model, and the training corpus of a language model, and can even write from scratch weights that defeat a PPO network trained for 100 million steps in three MinAtar games. On Qwen3.5-0.8B, Astra located the weight direction causing reasoning loops and, by modifying only 0.016% of the weights, raised MATH-500 from 7.9% to 48.1% and IFEval from 21.0% to 62.3%; however, for Gemma 3 270M, which rarely loops, it failed to improve math scores.