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.
第十七条 经登记的选民名单应当在选举日的二十日前公布；采取每户派代表或者居民代表选举方式的，应当同时公布户的代表或者新一届居民代表名单。
，详情可参考Line官方版本下载
[&:first-child]:overflow-hidden [&:first-child]:max-h-full"
SelectWhat's included。业内人士推荐旺商聊官方下载作为进阶阅读
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Раскрыты подробности похищения ребенка в Смоленске09:27，这一点在51吃瓜中也有详细论述