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在一个外观像核磁共振机的仪器旁，中医药广东省实验室副主任周华告诉记者：“这是零磁中医证候诊断装备。我们首次应用量子极弱磁技术检测中医证候状态下的功能信息，建立了零磁中医证候诊断技术平台。”，这一点在一键获取谷歌浏览器下载中也有详细论述
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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.