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.
前NBA球員恩尼斯·坎特·弗里德姆（Enes Kanter Freedom）稱她為「叛徒」，並指其「生於美國、長於美國、居於美國，卻選擇為全球最惡劣的人權侵犯者——中國——對抗自己的國家」。
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Раскрыты подробности о договорных матчах в российском футболе18:01
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Waning Gibbous - The Moon starts losing light on the right side. (Northern Hemisphere)
To be fair, much of that is by design. DTF St. Louis wants to keep viewers in the dark about its central investigation throughout its run. Unfortunately, that means dragging the mystery out, especially when it comes down to Homer and Plumb's investigation.，推荐阅读safew官方版本下载获取更多信息