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.
18:53, 27 февраля 2026Наука и техника
Мир Российская Премьер-лига|19-й тур，更多细节参见同城约会
Медведев вышел в финал турнира в Дубае17:59
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1970-1986年，是塔可夫斯基创作风格趋于成熟、美学和哲学思考走向深邃的16年，也是他与苏联制片体制不断拉扯、与自我反复博弈的16年。这些散落的私人絮语，为他的作品补上了鲜活的创作注脚。，这一点在WPS下载最新地址中也有详细论述