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“这是诈骗电话。”我对母亲说。可她将信将疑，嘴里嘟囔着：“可他说得挺真的……”
，这一点在一键获取谷歌浏览器下载中也有详细论述
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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记者调查发现，除了“主页圈”外，小天才手表体系还衍生出“运动圈”“破解圈”等多个社交圈。“破解圈”针对手表封闭系统，通过突破家长管控模式刷机，可让手表变身为功能齐全的迷你手机。网络上的破解教程随官方系统更新持续进阶，部分学生还会借助AI制定详细的刷机方案。