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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从进屋到现在，我才认真端详起阿爸来。阿爸今年54岁，也到了一半入土的年纪了。看起来他和前年没什么变化——一个标志性的光头，一身印着管道公司广告的衣服。冬天到了，里面套着绿的、蓝的、黄的里衣，什么颜色，全看哪件先晾干。，更多细节参见WPS下载最新地址
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