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.
第一百零九条 治安管理处罚由县级以上地方人民政府公安机关决定；其中警告、一千元以下的罚款，可以由公安派出所决定。
，这一点在雷电模拟器官方版本下载中也有详细论述
全国人大常委会副委员长李鸿忠、王东明、肖捷、郑建邦、丁仲礼、蔡达峰、何维、武维华、铁凝、彭清华、张庆伟、洛桑江村、雪克来提·扎克尔，秘书长刘奇出席会议。，这一点在Line官方版本下载中也有详细论述
但无论是 OLED 像素发光还是 LCD 背光板发光，它们发出的都是（近似）全向的光线——你能看到，你旁边的人也能看到：。关于这个话题，搜狗输入法2026提供了深入分析
吸食、注射毒品的，可以同时责令其六个月至一年以内不得进入娱乐场所、不得擅自接触涉及毒品违法犯罪人员。违反规定的，处五日以下拘留或者一千元以下罚款。