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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.。关于这个话题,雷电模拟器官方版本下载提供了深入分析
Москвичи пожаловались на зловонную квартиру-свалку с телами животных и тараканами18:04。safew官方下载对此有专业解读
食環署也非從未引用現有法律對寵物友善餐廳執法:2025年2月,大埔大元商場一家咖啡廳因兩度被抓獲允許狗隻進入,被法庭定罪罰款後,再被食環署吊銷牌照七天,引起輿論和狗主討論。