人 民 网 版 权 所 有 ，未 经 书 面 授 权 禁 止 使 用
Copyright © 1997-2026 by www.people.com.cn all rights reserved，详情可参考新收录的资料
— banteg (@banteg) February 1, 2026，推荐阅读新收录的资料获取更多信息
If you have a pair of AirPods or other earbuds that are wearing out and you need to replace them -- or you have a pair with older features and you're not happy with the sound or ANC quality -- I have little doubt that you would be very happy with a pair of AirPods 3, especially if you have multiple Apple devices.。新收录的资料是该领域的重要参考
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.