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.
昨天，OPPO 官方发布新一代折叠屏旗舰 Find N6 的预热海报，打出「一马平川」slogan，暗示其在折痕控制上取得突破。
。关于这个话题，搜狗输入法2026提供了深入分析
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12:54, 27 февраля 2026Ценности，这一点在搜狗输入法下载中也有详细论述