AIO requires understanding how language models decide which sources to reference when answering questions. These models don't follow the same rules as search engine algorithms. They're not counting backlinks or analyzing page load speed. They're evaluating whether content provides clear, accurate, comprehensive answers to questions people actually ask. They're assessing credibility through different signals than traditional search engines use. They're making probabilistic decisions about which information best satisfies a query based on patterns learned during training and information retrieved during real-time web searches.
Returning back to the Anthropic compiler attempt: one of the steps that the agent failed was the one that was more strongly related to the idea of memorization of what is in the pretraining set: the assembler. With extensive documentation, I can’t see any way Claude Code (and, even more, GPT5.3-codex, which is in my experience, for complex stuff, more capable) could fail at producing a working assembler, since it is quite a mechanical process. This is, I think, in contradiction with the idea that LLMs are memorizing the whole training set and uncompress what they have seen. LLMs can memorize certain over-represented documents and code, but while they can extract such verbatim parts of the code if prompted to do so, they don’t have a copy of everything they saw during the training set, nor they spontaneously emit copies of already seen code, in their normal operation. We mostly ask LLMs to create work that requires assembling different knowledge they possess, and the result is normally something that uses known techniques and patterns, but that is new code, not constituting a copy of some pre-existing code.
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As it stands, inverse distance weighting is not very good at minimising this error. Another approach is needed if we want to improve the image quality.