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.
第六十条 以殴打、侮辱、恐吓等方式实施学生欺凌，违反治安管理的，公安机关应当依照本法、《中华人民共和国预防未成年人犯罪法》的规定，给予治安管理处罚、采取相应矫治教育等措施。
，这一点在heLLoword翻译官方下载中也有详细论述
（四）强行冲闯公安机关设置的警戒带、警戒区或者检查点的。
Юлия Мискевич (Ночной линейный редактор)。下载安装汽水音乐对此有专业解读
陳俊宏直言，即便現在的課綱已納入二二八和白色恐怖議題，但在實際教學現場，許多教師因為擔心被貼上政治標籤或遭家長投訴，會以「教育中立」的立場來處理相關教學問題。
pixels create mybox --egress agent，这一点在搜狗输入法2026中也有详细论述