pixel[1] = pixel[1] 0.04045f ? powf((pixel[1] + 0.055f) / 1.055f, 2.4f) : pixel[1] / 12.92f;
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.
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花有品，人有格。从古至今，这朵淡黄小花被赋予了别样魅力，逐渐成为“不畏严寒，风骨高洁”的象征。。关于这个话题，夫子提供了深入分析
笔录由仲裁员、记录人员、当事人和其他仲裁参与人签名或者盖章。，这一点在safew官方版本下载中也有详细论述
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