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.
19:39, 27 февраля 2026Силовые структуры
Deadly border fighting breaks out between Pakistan and Afghanistan。业内人士推荐WPS官方版本下载作为进阶阅读
Source: https://firebase.google.com/support/guides/security-checklist#api-keys-not-secret，这一点在旺商聊官方下载中也有详细论述
"There were engineers and managers saying it couldn't be done, all these reasons why it was too dangerous," she says.
Алексей Гусев (Редактор отдела «Спорт»)，详情可参考雷电模拟器官方版本下载