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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GLU/SwiGLU 在实际中是门控形式（two linear branches），是向量上的逐元素操作；为了在一维上可视化，我用简化的标量形式来画图 —— 把两条分支都用相同的输入值（即把 a=x, b=x），因此 GLU(x)=x∗sigmoid(x) SwiGLU(x)=x∗SiLU(x) 。这能直观展示门控机制的形状差异。