Paper proposes weight space programming: LLMs can read and write neural network weights directly

On 3 November 2026, Latent Space's AINews rounded up work by Takehi et al. on Weight Space Programming. According to the report, the method lets frontier models inspect and edit the weight tensors of neural networks directly from natural-language goals, without relying on gradient descent or training loops, thereby bypassing the self-research loop. The report describes the work as letting LLMs read and write neural network weights directly. Public information so far comes only from this roundup, with no further details or follow-up progress.

Written by AI from the reports · updated 2 h ago

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