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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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“我们要按照习近平总书记的要求，持续巩固拓展脱贫攻坚成果，把常态化帮扶纳入乡村振兴战略统筹实施，守牢不发生规模性返贫致贫底线，夯实乡村全面振兴根基。”卢春涛说。
而表面上，这是由供应链波动引发的正常连锁反应，可从根源上讲，它透露出AI的技术浪潮在带来创新想象力的同时，可能也会给智能手机产业的发展造成难以预估的影响，或者说压力。