Publication date: 10 March 2026
春节期间，手里难免沾上油烟、糖霜或者护手霜，手机镜头大概率是蒙着一层油污的。带着油污去拍照，所有的灯光都会变成乱七八糟的眩光，画面也是雾蒙蒙的，再精通后期也救不回来。所以，在掏出手机准备记录美好瞬间之前，先用衣服下摆或者纸巾，用力地、仔细地把镜头擦干净。
，这一点在heLLoword翻译官方下载中也有详细论述
KlefkiIntroduced in Gen VI (2013)
Even though my dataset is very small, I think it's sufficient to conclude that LLMs can't consistently reason. Also their reasoning performance gets worse as the SAT instance grows, which may be due to the context window becoming too large as the model reasoning progresses, and it gets harder to remember original clauses at the top of the context. A friend of mine made an observation that how complex SAT instances are similar to working with many rules in large codebases. As we add more rules, it gets more and more likely for LLMs to forget some of them, which can be insidious. Of course that doesn't mean LLMs are useless. They can be definitely useful without being able to reason, but due to lack of reasoning, we can't just write down the rules and expect that LLMs will always follow them. For critical requirements there needs to be some other process in place to ensure that these are met.