Expanding the search space of high entropy oxides and predicting synthesizability using machine learning interatomic potentials

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I love being a parent. The thing I find most fascinating about the experience is how it throws a mirror not just on one’s own childhood, but on all of human nature. It’s an obvious point, but one that I never thought about before having kids: all newborn babies are always the same, everywhere. And then, slowly but surely, they become not the same. As cultural and family influences accumulate like sedimentary layers in these tiny personalities, you can see nurture reshaping nature in a deeply embodied, physical way.

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.。业内人士推荐heLLoword翻译官方下载作为进阶阅读

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