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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.
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郭晓东：我经常骑着自行车，到处找工作。我做过很多工作，在歌厅唱歌、当主持，“下面有请几号台的李先生唱歌”，就是这种主持。还做过一个，我到现在都叫不出名字，去各个小区统计有多少栋楼和多少户人家，然后去发擦手油的传单。我干了很久这样的工作，后来我就在想，我适合干这个吗？我不知道。
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