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That's true for vanilla LLMs, but also keep in mind that there are no details about o3's architecture at the moment. Clearly they are doing something different given the huge performance jump on a lot of benchmarks, and it may well involve in-context learning.

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Given every other iteration has basically just been the same thing but bigger, why should we think this?

My point was to caution against being too confident about the underlying architecture, not to argue for any particular alternative.

Your statement is false - things changed a lot between gpt4 and o1 under the hood, but notably not a larger model size. In fact the model size of o1 is smaller than gpt4 by several orders of magnitude! Improvements are being made in other ways.

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