两家的老人也同步了作息时间表，尽可能贴合流程来，有一些波动很正常，比如出门玩了，吃饭晚一些，午睡晚一些，都是允许的。但步骤不能缺，下午尽可能的不让她睡太多，防止晚上睡不着。
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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Natural neighbour interpolation has a number of strengths over linear barycentric interpolation. Namely, it provides a smooth or continuous slope between samples3, and is always the same for a given point set, unlike the TIN where the quality of the triangulation can produce biases in the outcome, even in the best case.