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.
第四十四条 举办体育、文化等大型群众性活动，违反有关规定，有发生安全事故危险，经公安机关责令改正而拒不改正或者无法改正的，责令停止活动，立即疏散；对其直接负责的主管人员和其他直接责任人员处五日以上十日以下拘留，并处一千元以上三千元以下罚款；情节较重的，处十日以上十五日以下拘留，并处三千元以上五千元以下罚款，可以同时责令六个月至一年以内不得举办大型群众性活动。，这一点在safew官方版本下载中也有详细论述
，详情可参考safew官方版本下载
斯塔默表示，做出此类决策需要确保英国的行动有合法依据。他还表示，虽然美国总统特朗普对英国未参与对伊朗的打击表示不满，但英国需考虑自身国家利益。
Author(s): Qin Qin, Yawen Hua, Luyao Hai, Meidie Wu, Siqi Jiang, Rongxing Ye, Jiangfeng Song, Yiliang Liu, Linsen Zhou，这一点在体育直播中也有详细论述
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