GPT-6 Astra sees the matrix and reprograms other AI models directly through their weights

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MIT researchers show that GPT-6 Astra can directly understand and modify the internal settings of other AI models without traditional training. The approach has limits but could change how AI systems are tuned and controlled.

Hand a person a list of thousands of numbers and ask what game an AI model trained on them plays. You won't get an answer. GPT-6 Astra from OpenAI will give you one: In a research blog, MIT researchers show that GPT-6 Astra can analyze and deliberately modify the internal settings of other AI models, the so-called weights.

These numerical values are organized in massive tables, or matrices in mathematical terms.

The usual training process of step-by-step mathematical optimization isn't needed. Depending on the experiment, Astra worked directly with lists of numbers or used programming tools and feedback from test runs.

The researchers call the approach "Weight-Space Programming": instead of expensively retraining an AI model, a language model acts like a mechanic turning individual knobs.

In several experiments, the researchers fed Astra the raw weights of trained networks and asked what those networks had learned. Astra correctly identified which of four computer games a network was supposed to play. It also recognized the basic shapes an image generator produced, though it confused filled shapes with outlines.