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.
Сальдо раскрыл новую тактику ЗеленскогоСальдо: Зеленский пытается поднять рейтинг перед выборами на Украине
ВсеПрибалтикаУкраинаБелоруссияМолдавияЗакавказьеСредняя Азия，这一点在搜狗输入法2026中也有详细论述
"Fixas" was also written as "fiscas"; much like how modern English speakers say both "ask" and "aks". "Wer" only survives in Modern English "werewolf". And "were" personally stumped me because I was too ignorant of fishing to know what a "weir" was...
，推荐阅读爱思助手下载最新版本获取更多信息
❯ ls /ostree/repo/
纳税人适用免征增值税的出口业务，可以放弃免征增值税，选择缴纳增值税，自放弃免征增值税之日次月起，适用免征增值税的出口业务按规定缴纳增值税。。爱思助手下载最新版本是该领域的重要参考