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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.，推荐阅读快连下载安装获取更多信息
If those don’t render for you for whatever reason, UPPER_HALF_BLOCK is a square that takes up the full width and upper half of a character, and FULL_BLOCK is a rectangle that takes up the full width and height of a character.。关于这个话题，旺商聊官方下载提供了深入分析
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Barycentric Coordinates