StudioSparks of Weight-Space Programming: GPT-6 Astra natively reads and writes neural network weights, fixing Qwen3.5-0.8B without gradients
Original title: Sparks of Weight-Space Programming: LLMs can natively read & write neural network weights
The authors propose Weight-Space Programming, testing whether frontier LLMs (GPT-6 Astra) can directly read and write neural network weights. Experiments show it can identify a network's trained task from weights alone, write game-playing networks from scratch that beat PPO trained for 100 million steps, and fix Qwen3.5-0.8B's reasoning loops without gradients by editing just 0.016% of weights, raising MATH-500 from 7.9% to 48.1% and IFEval from 21.0% to 62.3%. Full experiment logs are open-sourced at github.com/RikiyaT/lm_understand-logs, and the edited model is released on Hugging Face.
The post reports hands-on experiments of frontier LLMs directly reading and writing network weights, including the concrete fix for Qwen3.5-0.8B's reasoning loops, with methods verifiable in released logs.
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