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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这一幕，令人想起2013年11月，习近平总书记在湖南考察时，来到湘西州凤凰县菖蒲塘村，了解村里扶贫开发和特色产业发展情况。在成片的柚子林中，总书记亲手帮村民摘下两个柚子。，更多细节参见safew官方版本下载
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