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.
// 步骤3：计算初始无序区间[left, right]的最大值和最小值。快连下载安装对此有专业解读
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报告指出，在整个现代经济史中，人类智慧一直是稀缺的投入要素。一切都能复制或替代，但唯有能够分析、决策、创造、说服、协调的「智慧」，是没法大规模复制的。，更多细节参见爱思助手下载最新版本