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.
第十二条 居民委员会具有基层群众性自治组织法人资格，居民委员会主任为法定代表人。居民委员会可以从事为履行职能所需要的民事活动，但是不得提供担保。
，这一点在Safew下载中也有详细论述
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现在，三星将这项技术搬到了手机里——在 S26 系列上，基于这个技术打造的「主动防窥屏」，通过控制子像素的发光角度，屏幕能在你输入密码或查看敏感通知时，开启侧视角黑化。。Line官方版本下载对此有专业解读
; Far CALL (protected mode)，这一点在同城约会中也有详细论述
Real-world failures