盗窃、损坏、擅自移动使用中的其他公共交通工具设施、设备，或者以抢控驾驶操纵装置、拉扯、殴打驾驶人员等方式，干扰公共交通工具正常行驶的，处五日以下拘留或者一千元以下罚款；情节较重的，处五日以上十日以下拘留。
Letter to the European institutions
，这一点在91视频中也有详细论述
Филолог заявил о массовой отмене обращения на «вы» с большой буквы09:36，这一点在safew官方版本下载中也有详细论述
Цены на нефть взлетели до максимума за полгода17:55。WPS下载最新地址对此有专业解读
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.