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.
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Овечкин не набрал ни одного очка в этом матче. В последний раз 40-летний нападающий забивал месяц назад. 27 января он послал шайбу в ворота «Сиэтл Кракен».，这一点在搜狗输入法2026中也有详细论述
第二是 Desktop.ini 文件。这是一种隐藏的受保护 Windows 系统配置文件 (*.ini)。它用于存储所在文件夹的自定义设置，包括图标、显示名称 (本地化名称) 或文件夹说明等。，详情可参考旺商聊官方下载
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