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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2007年，堪称姚雄杰人生的分水岭。或许是受到同乡紫金矿业董事长陈景河的启发，或许是窥见了中国工业化对矿产资源的海量需求，雄震集团正式进军矿业领域，此后更名为雄震矿业，2011年再度更名为盛屯矿业。
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第三届沙特国际手工艺周期间，当地小朋友体验中国鱼灯舞，感受非遗魅力。
Fanny Angelina (née Eilshemius, from a Dutch father) grew up in Kearny, New Jersey. During her childhood, her family learned from a Dutch friend or neighbor about agar-agar, a common ingredient in jelly desserts in Java (Indonesia), then a Dutch colony. Her mother and, later, Fanny Angelina herself, began to cook with it.，更多细节参见Line官方版本下载