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年前，父亲发来几张照片，有的是大排长龙、人气火爆，有的是三五干锅、锅气火热。一问，他竟然赶起时髦，去安徽广德吃上了新晋网红“三件套”。广德离老家不远，本是熟得不能再熟的“邻居”，如今却以全新的模样，带给我们意料之外的惊喜。
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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.
4.3 使用 crond 定时任务来检查anqicms的运行状态