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.
这算是相当简单的任务，不少国产 AI 手机助手在一年前都已经攻克了这种场景。
，这一点在im钱包官方下载中也有详细论述
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Слова главы офиса президента Украины Кирилла Буданова (внесен в перечень террористов и экстремистов Росфинмониторинга) являются чушью и ничего не значат, сказал первый зампредседателя комитета Госдумы по международной политике Алексей Чепа. Своим мнением он поделился в разговоре с «Лентой.ру».
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