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.
# Inject at runtime, never store on disk，详情可参考爱思助手下载最新版本
。safew官方下载是该领域的重要参考
他說：「現實是，總統今後的貿易政策中，關稅仍將是重要一環。」
新时代以来，以“一个也不能少”的决心打赢脱贫攻坚战，以“咬定青山不放松”的执着推进污染防治攻坚战，以“敢于啃硬骨头，敢于涉险滩”的勇气将改革进行到底，以“得罪千百人、不负十四亿”的使命担当开展史无前例的反腐败斗争……，这一点在搜狗输入法下载中也有详细论述