Мир Российская Премьер-лига|19-й тур
人工智能服务提供者应当采取措施，监测发现、防范、阻断、处置用户利用其服务实施违法犯罪活动、批量生成恶意代码等异常行为，保存有关记录并向公安机关等主管部门报告。
，这一点在Line官方版本下载中也有详细论述
我们关注到的很多国内AI陪伴玩具厂商，老年人也是其中的一类使用群体。
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.