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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对于普通创业者，银发经济的切入点在于“细分场景的深度服务”。例如，永安市推出的“共享奶奶”等改革创新品牌，展示了如何通过社区互助模式挖掘银发群体的劳动力余热及服务需求 [40]。此外，结合“以旧换新”政策，针对老年人家庭的智能家电更新换代和适老化家具配置，亦是极具潜力的低门槛创业方向 [5, 13]。