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.
一名不具名的澳洲政府消息人士告訴澳洲廣播公司（ABC），大眾不應「妄下結論」，並指出這項炸彈威脅也有可能來自社區中對「法輪功」抱有敵意的人。
Дания захотела отказать в убежище украинцам призывного возраста09:44，详情可参考搜狗输入法2026
For multiple readers。关于这个话题，谷歌浏览器【最新下载地址】提供了深入分析
class DatabaseStorage(Storage):
encrypting and unlocking crypto wallets。业内人士推荐heLLoword翻译官方下载作为进阶阅读