Source: Latent Space
Large language models can now manipulate the internal parameters of other neural networks without retraining—a capability called "weight space programming" that bypasses the traditional autoML optimization loop. Instead of running expensive hyperparameter searches or architecture searches to improve a model, you can prompt an LLM to surgically alter weights based on what it understands about how networks function. This removes a computational bottleneck: faster model iteration, lower cost, and LLMs acting as direct modifiers of AI systems rather than interfaces to them. The reliability and interpretability of LLM-generated weight edits at scale remain untested.