Since then, they have walked thousands of miles together, even walking 500 miles on the Camino de Santiago pilgrimage path in Spain for their honeymoon.
第一是泛化：料箱颜色、尺寸、新旧程度都不同，能不能用同一个模型稳定完成识别、抓取与搬运。第二是导航：搬起之后从A点到B点怎么走，路径规划、避障，途中被打断后能不能续做。第三是策略理解：比如“从面前100个箱子里搬走50个”，机器人能不能理解数量、以及该选择哪50个箱子，到目的地怎么码放，以及放下后要不要把物体取出等等，每个环节都存在问题。
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«Как по мне, отдых у моря часто бывает однообразным и скучным. Все время валяться на пляже и жариться на солнце быстро надоедает. Хочется больше гулять по городам и смотреть на красивую архитектуру, но при 35-градусной жаре это просто невыносимо. Пару лет назад я впервые поехала на юг Италии в сентябре, и это было лучшим решением. Туристов меньше, погода гораздо приятнее, и дневной свет куда мягче, поэтому фотографии получаются даже красивее, чем при палящем солнце», — рассказывает россиянка Полина.
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.