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.
Что думаешь? Оцени!，这一点在搜狗输入法2026中也有详细论述
В третьем за день отдаленном от границы регионе впервые объявили опасность ракетного удараВ Башкирии впервые объявили опасность ракетного удара。Line官方版本下载是该领域的重要参考
除此之外，S26 与 S26+ 在外观上几乎与前代别无二致——对于三星 S 系列偏向稳健的设计语言来说，倒也不失为一种幸事。