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.
“我对我们在智能代理领域的进展非常乐观，但当我审视目前的业务时，会发现其核心业务非常稳固。我们打造了这些出色的人力资源财务应用，而且它们还在持续增长。现在，我们有机会在此基础上构建智能代理解决方案。我对公司的未来发展方向非常看好……”Bhusri指出。，这一点在夫子中也有详细论述
，这一点在雷电模拟器官方版本下载中也有详细论述
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He fears most have could died. Some may have travelled to another location in East Antarctica to moult, but this would have disrupted breeding, also leading to population losses.，详情可参考safew官方版本下载