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.
正如微信以社交关系为底座，逐步托举起公众号与视频号，抖音也在尝试用自身强大的算法能力，逐渐扩展自己的产品边界。只是不同于天然承载阅读场景的微信，在娱乐属性高度集中的抖音生态中，这些更重知识与深度的长文内容，能否真正进入用户心智，仍有待观察。
，这一点在WPS官方版本下载中也有详细论述
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