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Российские войска наступают на стыке Запорожской и Днепропетровской областей. Об этом сообщил Telegram-канал «Иди и смотри».
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苹果，是满足人民美好生活需要的“健康果”，更是振兴乡村经济的“致富果”。在陕西延安，习近平总书记深情寄语：“这是最好的、最合适的产业，大有前途。”在甘肃天水，“把这个特色产业做得更大”，总书记的关怀为苹果产业注入了强劲动力。
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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.