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.
今年春節前，海外留學歸來的高先生推開家門，看到的景象讓他愣在原地：母親一手握着手機，在窗台前的水晶堆裏挑挑揀揀。從大到小的水晶擺件佔滿整個窗台，都是母親幾個月來從直播間「淘」回來的。更讓他擔憂的是，母親肩頸疼痛已嚴重到走路、站立都成問題，總是佝僂着背。
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