William Emeny

guys, this is a serious breakthrough. Please read. Researchers at MIT CSAIL just showed that GPT-6 Astra can read and edit the raw weights of another neural network directly. Weights are the huge set of numbers that make up an AI model. To a person they look kinda scary, so nobody edits them by hand. If you want a model to improve, you normally train it by showing it lots of examples and adjusting those numbers little by little. Nearly all of modern AI is built this way, and it takes a lot of time and compute. In this work, they skipped training completely. They asked Astra to make Qwen3.5-0.8B better at math, with no gradients and no training. Astra looked at the weights, found an issue linked to the model getting stuck in reasoning loops, and changed only 0.016% of them. Its MATH-500 score went from 7.9% to 48.1%, about 6x higher, and its instruction following score tripled as well. The researchers also gave Astra the raw weights of four game-playing networks with identical structures. With no tools and no list of options, it correctly named all four games: Breakout, Pong, Pac-Man and Freeway. Then, given the rules and code for a small version of Breakout, it wrote a network's weights from scratch in a single reply. That network scored 517.4, while one trained with PPO for 500 million steps scored 25.3. With normal software, you can read the code, find the bug and fix that one thing. With neural networks, you have mostly had to retrain them and hope the problem goes away. This work suggests weights might start to behave more like code. If that holds up, you could fix some model problems in one edit instead of a long training run, inspect what a model does by reading it directly, and let AI improve other AI in a much more direct way. It is still early. The authors themselves call these early signs, the game networks are tiny, and the Qwen fix targeted a flaw its model card already warned about, so this does not mean AI can rewrite any model at will yet. It needs to be replicated and tested on bigger models. But AI reading another network's weights and improving it this much with no training is a big deal, and I think a lot more people should be paying attention.

We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics. The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra. The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.

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