Овечкин продлил безголевую серию в составе Вашингтона09:40
Maggie 姐叱咤夜场25年，看遍风云变幻、人生百态，她对自己的事业仍抱有热忱（图：南方人物周刊记者 方迎忠）。关于这个话题，heLLoword翻译官方下载提供了深入分析
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一项针对超过8000块电动汽车电池的研究得出结论：大多数电池的使用寿命已经超过了它们所搭载的车辆本身。这一发现极大地缓解了长期以来围绕电池耐用性的普遍担忧。
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.