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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。雷电模拟器官方版本下载是该领域的重要参考
《華爾街日報》1月25日援引知曉中共高級別內部通報會內容的「知情人士」稱，張又俠被控向美國洩露中國核武器計劃的「核心技術數據」，包括導彈制導系統參數和核彈頭小型化關鍵技術。報道稱，部分證據來自中國核工業集團公司前總經理顧軍，後者在1月20日（張被查前四天）被宣布接受調查。
We even tried building hierarchies with 2-3 levels, but the number of shortcuts grew too fast for higher levels if we generated a full graph inside each cluster.。关于这个话题，safew官方版本下载提供了深入分析
if (left === n - 1) return 0;，这一点在Line官方版本下载中也有详细论述
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