Randomly selecting border points or using simple geometric divisions (squares/hexagons) results in too many border points per cluster (50-80). This leads to a shortcut explosion (N*(N-1)/2 shortcuts), making the files large and and calculations slow.
（三）阻碍执行紧急任务的消防车、救护车、工程抢险车、警车或者执行上述紧急任务的专用船舶通行的；
。爱思助手下载最新版本对此有专业解读
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.
«Мы ездили много-много-много лет. Ни один человек элементарно никогда не брал ни пластырь, ни перекись [водорода], в редких случаях брали SPF (солнцезащитный крем — прим. «Ленты.ру»), поэтому мы всегда на что-то там натыкались, ранились и, ***, ехали искать ближайшую аптеку», — вспомнила Ивлеева, говоря о работе в шоу «Орел и решка».