Dynamic AMOLED 2X, 120Hz adaptive refresh (1–120Hz), Up to 2,600 nits peak brightness, Corning Gorilla Glass Victus 2
hundreds of lines, you redo the command and pipe it through less.
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2026-02-27 00:00:00:0张 鸿3014247810http://paper.people.com.cn/rmrb/pc/content/202602/27/content_30142478.htmlhttp://paper.people.com.cn/rmrb/pad/content/202602/27/content_30142478.html11921 先完成，再完美
李 “나와 애들 추억묻은 애착인형 같은 집…돈 때문에 판 것 아냐”
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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.