3014311610http://paper.people.com.cn/rmrb/pc/content/202603/03/content_30143116.htmlhttp://paper.people.com.cn/rmrb/pad/content/202603/03/content_30143116.html11921 “不断突破战后和平发展体系，必将日本推向危险边缘”（国际视点）
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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.
出席重要会议，主持集体学习，赴地方考察调研，习近平总书记深刻阐释全会精神，为“十五五”规划《纲要草案》编制和各地各部门做好实际工作提供根本遵循。