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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My Voyager keyboard and Ploopy Adept trackball are mounted to a telescoping section of T-slot that allows me to stand about 1.5 meters / 5 feet away from the monitors. This reduces eyestrain from working at a computer all day and, hopefully, helps slow the progression of myopia. I used ZSA’s Tripod Mount kit and standard ball-head tripod mounts to connect the keyboard and mouse to the T-slot. This makes for easy, secure tenting of the keyboard and mouse at the perfect angle.
带好工作证的Maggie姐站在一边，她的额头悄悄渗出汗来，这位身经百战的女强人难得碰上让她紧张的时刻。作为公关经理，她还要为查牌时间担忧。通常80个小姐的查牌时间是一小时左右，按每人500块计算，这一个小时里，公司至少将损失4万块。让Maggie姐惊喜的是，这夜的查牌时间仅为15分钟。。业内人士推荐safew官方版本下载作为进阶阅读