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.
See sessions of two related sites as a single session，这一点在51吃瓜中也有详细论述
Дания захотела отказать в убежище украинцам призывного возраста09:44。关于这个话题，快连下载安装提供了深入分析
至于生父，他只记得，村里人都叫那个人“客边”，本地方言里，外地人的意思。他也跟着这么叫，“客边来了，客边走了”。。Line官方版本下载对此有专业解读