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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在2025年12月召开的中央经济工作会议上，习近平总书记指出：“脱贫攻坚取得胜利后，有一个过渡期，今年完成了，但完成不代表任务取消了。仍然要把‘三农’工作作为重中之重，抓好巩固拓展脱贫攻坚成果同乡村振兴的衔接；仍然以乡村振兴、‘三农’工作的发展作为中国式现代化的底座。”
// 倒序遍历：从最后一个元素开始（核心思路！易错点1）