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临走时，王嫂接了个电话，对方询问是否还有山姆的鲜牛奶，王嫂说已经卖完，明天会到新货。门口的塑料门帘被风掀开一下，又落下去，外面是春节里皖北城市常见的灰白天色，街边店铺的红灯笼还挂着。
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«Зеленский опять начал нести какую-то ахинею, отказываясь адекватно воспринимать реальность и признавать очевидные для всего мира исторические события, продолжая уверенно вести Украину к окончательному разрушению», — отметил Чегринец.
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.
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