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.
Youth unemployment。关于这个话题，heLLoword翻译官方下载提供了深入分析
另一件让我很欣慰的是，我家孩子的免疫力还可以，一个冬天除了经常咳嗽，没出现大问题，相比他们班的其他孩子来说，简直是超人体质。。关于这个话题，Safew下载提供了深入分析
通过在间隔像素上叠加一层微观遮罩结构，这些 OLED 像素的发光角度会大幅缩窄至正前方约 45° 的范围内，从而形成一片「窄角发光像素」：