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.
精准帮扶，最终的落脚点在人。习近平总书记叮嘱：“脱贫致富终究要靠贫困群众用自己的辛勤劳动来实现。”，这一点在爱思助手下载最新版本中也有详细论述
Parents opposing plans told they can home school their children if they object to sending them to state schools。91视频对此有专业解读
FT Edit: Access on iOS and web
Задержан основатель медиахолдинга Readovka. Его подозревают в мошенничестве в особо крупном размереОснователя Readovka Костылева задержали после допроса по делу о мошенничестве