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.
第一百三十五条 公安机关依法实施罚款处罚，应当依照有关法律、行政法规的规定，实行罚款决定与罚款收缴分离；收缴的罚款应当全部上缴国库，不得返还、变相返还，不得与经费保障挂钩。。关于这个话题，51吃瓜提供了深入分析
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Юлия Мискевич (Ночной линейный редактор)