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.
:first-child]:h-full [&:first-child]:w-full [&:first-child]:mb-0 [&:first-child]:rounded-[inherit] h-full w-full
，推荐阅读WPS官方版本下载获取更多信息
Samsung Galaxy S26 vs. S24: After comparing both models, here's my take。服务器推荐是该领域的重要参考
第六十四条 电信、金融、互联网等服务提供者者不履行本法规定的网络犯罪防治义务，侵害众多个人的合法权益，或者致使国家利益、社会公共利益受到损害的，人民检察院、有关主管部门以及相关社会组织可以依法向人民法院提起公益诉讼。