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.
從禮貌待人到假裝自己身處《星際迷航》（Star Trek，《星際爭霸戰》）的場景中，關於如何與聊天機器人對話的建議可謂五花八門，而且完全沒有用。以下這些才是真正有效的方法。，这一点在雷电模拟器官方版本下载中也有详细论述
Bits [13:2]: A 12-bit microcode redirect address -- a fault handler (e.g., 0x85D for #GP, 0x870 for #NP) or a gate dispatch routine (e.g., 0x5BE for a 386 call gate).，详情可参考服务器推荐
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