Ethan Mollick Says Weight-Space Programming Claim Needs Replication

1. 08:06
   Baoyu

   Researchers Describe Weight-Space Programming for Editing Neural Network Weights

   A blog by four MIT CSAIL researchers describes Weight-Space Programming, in which GPT-6 Astra directly edited Qwen3.5-0.8B weights without training or gradients. The researchers report that changing 0.016% of weights raised MATH-500 accuracy from 7.9% to 48.1%, after Astra used 50 practice problems to address repetitive reasoning loops. They also report mixed results across game networks and say the findings have not been independently replicated.

2. 03:31
   Ethan Mollick · X

   Ethan Mollick Flags Weight-Space Programming Claim as a Big Deal Pending Replication

   Waiting for replication but this is a pretty big deal. Quoted post by @rikiyatakehi: We asked GPT-6 Astra to edit Qwen3.5-0.8B weights to improve math. It directly changed only 0.016% of weights, fixing 0.8B's reasoning loops & improving math up to 6x and even instruction following 3x, without gradients or training. We call this Weight-Space Programming: frontier LLMs can now natively read/write neural network weights! 🧵 Blog: https://rikiyat.github.io/blog/weight-space-programming/

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