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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Москвичам предсказали холодаСиноптик Леус: Температура в Москве в марте может быть аномально низкой，这一点在搜狗输入法下载中也有详细论述
第十六条 年满十八周岁的居民，不分民族、种族、性别、职业、家庭出身、宗教信仰、教育程度、财产状况、居住期限，都有选举权和被选举权；但是，依照法律被剥夺政治权利的人除外。
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