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.
Размер шрифта он рекомендовал корректировать под собственное удобство, чтобы не нужно было постоянно щурится или «прилипать носом» к монитору.
，这一点在快连下载-Letsvpn下载中也有详细论述
Жители Санкт-Петербурга устроили «крысогон»17:52
第十七条 行政执法监督机构对行政执法主体资格进行确认，对经确认有行政执法主体资格的，按程序向社会公示。
A quadtree does the same thing for two-dimensional space. It takes a rectangular region and divides it into four equal quadrants: northwest, northeast, southwest, southeast. If a quadrant has too many points in it, it subdivides again and again. Each subdivision creates smaller and smaller cells where points are densely packed.