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.
Цены на нефть взлетели до максимума за полгода17:55
。safew官方下载对此有专业解读
Дания захотела отказать в убежище украинцам призывного возраста09:44
Continue reading...。关于这个话题，safew官方版本下载提供了深入分析
这是不少网友在昨天 Nano Banana 2 推出后，体验完的明显感受。一方面部分场景下的生成速度有所提升，还有文本渲染的表现也更稳定，但是图片的美学表现，却似乎还不如之前的 Pro 版本。，推荐阅读Line官方版本下载获取更多信息
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