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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.，更多细节参见旺商聊官方下载
英國南極考察局旗下的各研究站負責監測不同的野生動物與環境議題。「哈利六號」專注於太空與大氣資料，同時研究其所在的布倫特冰架（Brunt Ice Shelf ），以及地球臭氧層空洞。
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RadialB's fake videos portray grimy Croydon waterparks and an arcade machine filled with knives
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