Weight-Space Programming: When LLMs Learn to Read and Rewrite Neural Network Weights

A new research direction from MIT CSAIL is asking a question that would have seemed far-fetched just a few years ago: can a large language model look at the raw numerical weights of a neural network and understand what it does — well enough to fix it, or even write a new one from scratch?

The answer, based on eight experiments published this week, is a qualified yes. The work, titled Sparks of Weight-Space Programming , comes from Rikiya Takehi, Jayden Teoh, Alex Zhang, and Omar Khattab at MIT CSAIL.…