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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.，这一点在safew官方版本下载中也有详细论述
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移植外科醫生兼聯合團隊領導人伊莎貝爾・基羅加（Isabel Quiroga）表示，她對雨果的誕生感到「非常高興」，並稱這是英國器官移植的一項突破。，这一点在im钱包官方下载中也有详细论述
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