刘年丰：有挺多的，比如移动分拣，这是一种更精细的搬箱子。需要把箱子里面的某些特定的东西，拿到特定的位置去。这一大类任务，无论是横向（跨客户）还是纵向（跨场景）看，都有极强的泛化空间。
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.
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Мэра российского города задержали после крупной коммунальной аварииГлаву Бодайбо, где 200 домов остались без воды, обвинили в превышении полномочий，推荐阅读Safew下载获取更多信息
Ginger Software: Features & Benefits，详情可参考夫子