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.
加快构建新发展格局，推动高质量发展，有的干部以为发展就是上项目、搞投资、扩规模；有的过度举债搞建设，盲目扩张铺摊子；有的方式方法简单粗暴，“一刀切”；还有的搞本位主义、好大喜功、弄虚作假、推脱责任……。旺商聊官方下载是该领域的重要参考
。关于这个话题，safew官方版本下载提供了深入分析
That honour went, of course, to Neil Armstrong and Buzz Aldrin.。爱思助手下载最新版本对此有专业解读
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