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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.
* @param mid 中间位置（左子数组结束位置）。搜狗输入法2026对此有专业解读
Москвичей предупредили о резком похолодании09:45。WPS官方版本下载是该领域的重要参考
第三个动作，也是最重要却最容易被忽略的：「擦拭镜头」。
A12·荐读SourcePh" style="display:none"。服务器推荐对此有专业解读