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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.，推荐阅读雷电模拟器官方版本下载获取更多信息
，这一点在WPS下载最新地址中也有详细论述
«Первая проблема — это проблема границы. Она фактически прозрачна и не признается Афганистаном, так как разделяет пуштунский народ на два государства», — напомнил собеседник.，推荐阅读51吃瓜获取更多信息
Российский арбитр Сергей Карасев вновь захотел поработать в Лиге чемпионов. Об этом он заявил в интервью «Чемпионату».
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