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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.
Hans-Christoph Steiner
春节期间，无论是孩子放鞭炮，还是全家人举杯的瞬间，都稍纵即逝。只拍一张照片，很容易遇到闭眼、表情管理失败的情况。打开实况照片，它能记录下快门前后 1.5 秒甚至 2 秒的画面。拍完后，你可以在相册里重新选择「关键帧」，总能挑出一张表情最完美的。