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.
时间回到2004年2月，主政一方的习近平同志参加中央党校省部级主要领导干部专题研究班。
63-летняя Деми Мур вышла в свет с неожиданной стрижкой17:54。关于这个话题，91视频提供了深入分析
And with the arrival of the Brit Awards on the horizon, Co-op Live appears to have flourished after its rocky start.，详情可参考同城约会
“정원오, 쓰레기 처리업체 후원 받고 357억 수의계약”，这一点在搜狗输入法2026中也有详细论述
provides all subscribers with basic tracking capabilities, making it suitable