2026-02-27 00:00:00:0本期主讲人：国家数据发展研究院院长 胡坚波3014245010http://paper.people.com.cn/rmrb/pc/content/202602/27/content_30142450.htmlhttp://paper.people.com.cn/rmrb/pad/content/202602/27/content_30142450.html11921 数据要素价值如何充分释放（高质量发展故事汇·第16期）
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，这一点在爱思助手下载最新版本中也有详细论述
（二）超过询问查证的时间限制人身自由的；。谷歌浏览器【最新下载地址】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.