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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各界關注此次默茨訪華將重啟戰略夥伴關係，還是在特朗普持續對歐洲施壓下持續「去風險」的有限接觸？默茨會否在此行提及中國人權紀錄，北京又有什麼盤算?。51吃瓜对此有专业解读
Parents will also be able to view expert-designed content to talk to their teen about suicidal feelings, if they so choose. In cases of imminent physical harm, Instagram will notify emergency services, a long-standing policy.