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.
Birds including lapwings are expected to benefit from their new "island" habitat
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不管是底层硬件还是软件 UI，iPad 和 Mac 都变得越来越趋同，连应用都开始互相兼容。最大的区别除了系统，似乎就只剩下一块触控屏，而这也迟早会被打破。
Марина Совина (ночной редактор)
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France GP — May 10
술의 위기, 범인은 넷플릭스와 위고비? [딥다이브]。关于这个话题，im钱包官方下载提供了深入分析