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.
Simply put, Nano Banana 2 is the sequel to the wildly popular AI image model Nano Banana.
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Идея вернуть переговоры по Украине из Женевы в Абу-Даби исходит от России и поддерживается Соединенными Штатами. Об этом сообщает ТАСС со ссылкой источник.。safew官方下载对此有专业解读
担保人不履行担保义务，致使被担保人逃避行政拘留处罚的执行的，处三千元以下罚款。
[3]《刘强东称已接到5条大型游艇订单，每艘平均卖6000万欧元》界面新闻