Researchers have used an artificial-intelligence model to read and tinker with the internal workings of another AI network — offering a new way to peek into the ‘black box’ of AI.
Computer scientists usually improve models by training them in tiny increments or by building them from scratch. A team at the Massachusetts Institute of Technology (MIT) in Cambridge has demonstrated how an AI model called GPT-6 Astra can interpret the raw internal values of some other models well enough to predict what different parts of the systems do. It can then edit the models to make improvements — including boosting their mathematical abilities. The authors reported their findings in a preprint1 on 2 November.
Frontier AI models are “showing early sparks” of an ability to treat neural networks like files that they can read, reason about and change, says co-author Rikiya Takehi, a machine-learning researcher at MIT.
The results do not mean that Astra, developed by OpenAI in San Francisco, California, can explain or understand how a given AI network works. But the findings suggest that general-purpose AI models — such as ChatGPT or Claude — might be able to modify a network in useful ways, like a mechanic who can identify and fix issues in an engine that they neither designed nor fully
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