Qwen 3.8 27B on my laptop can interpret model weights, too (simonwillison.net)

A recent paper titled "Sparks of Weight-Space Programming" reveals that advanced large language models (LLMs), including GPT-6 Astra, can interpret and modify neural network weights directly, marking a significant step forward in AI/ML capabilities. This process, dubbed "Weight-Space Programming," allows LLMs to analyze the numerical representation of a model and understand its training dynamics, enabling them to make effective changes without traditional training loops. For instance, GPT-6 Astra successfully identified and corrected issues in the Qwen 3.5-0.8B model's reasoning capabilities, boosting its performance significantly in mathematical tasks. The significance of this advancement lies in the potential to streamline model refinement and improve AI adaptability. The findings suggest that LLMs can engage in complex tasks beyond mere text processing by leveraging a model's raw weights. While some experts caution against overstating this capability, arguing that human-like reasoning underlies these processes, the ability of models like Qwen 3.8 27B to replicate such analyses indicates a promising trajectory for autonomous AI development. This research invites further exploration into the boundaries of LLM functionality and raises questions about future AI systems' operational paradigms.