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.
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Our house is small and the desk is in the thoroughfare that was intended as a dining space, so tidy cable management was a priority for me. I'm quite pleased with how invisible the cables are from any angle you look at the desk. I had plans to get a shoji screen to partition the space off a bit and hide the back of the desk, but the desk looks so good I don't want to hide it!
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陆逸轩：这大概就是我的性格吧，我不是那种会跳起来庆祝的人。宣布第一名的那一刻实在太“重”了，瞬间的冲击非常强。至于抓头发，其实完全是下意识的动作，我的手放在脸边时，常会碰一下头发，这就是习惯吧，也没有经过任何思考。那是一个非常情绪化的时刻，我所有的感受都在内心里，没有时间思考，只能去接受和感受那个情绪。，更多细节参见旺商聊官方下载