Трамп высказался о непростом решении по Ирану09:14
if (deflate.result) yield [deflate.result];
，详情可参考同城约会
（三）未经批准设置无线电广播电台、通信基站等无线电台（站）的，或者非法使用、占用无线电频率，从事违法活动的。
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.
，这一点在服务器推荐中也有详细论述
"That's obviously a lot to do with being a woman in music and being a pop star, or whatever, but I will say that if I wasn't down to do it, then I wouldn't."
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