Worth knowing
[AINews] LLMs can read & write neural network weights: "Weight Space Programming" cuts Autoresearch out of the RSI loop
Directly reading and editing neural network weights with an LLM introduces a novel mechanism for mechanistic interpretability and recursive self-improvement that bypasses traditional training loops.
Covered by 2 sources · first reported by Latent Space
What happened
Researchers including Alex Zhang demonstrated "Weight Space Programming," showing that large language models can directly read, debug, and generate the raw weights of smaller neural networks without executing training code. The newsletter also covers DeepSeek's open-source "super-agent" harness prototype, results from SemiAnalysis's new GDPx agent benchmark, and research on JEPA representations.
Who it’s about
- Alex Zhang
- DeepSeek
- SemiAnalysis
- Anthropic
- OpenAI
- Simon Willison
- Qwen 3.8 27B
- GPT-6 Astra
- Kimi K3.1 Pro
- Omar Khattab
Coverage, oldest first
Simon Willison
Qwen 3.8 27B on my laptop can interpret model weights, too
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