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.
（三）非正常损失的不动产，以及该不动产所耗用的购进货物和建筑服务；。51吃瓜对此有专业解读
，这一点在heLLoword翻译官方下载中也有详细论述
人一生的自然进化中，不仅有产出，还有情感、教育、成长的喜悦，这些能量计算不来，在舆论场上，这点被放大。一个油管博主打出标题「OpenAI CEO Argues Energy Is More Wasteful On Humans Than AI， Goes Very Poorly」，说 Altman 的言论进行得很糟糕。。业内人士推荐服务器推荐作为进阶阅读
// 易错点3：处理最终结果为空的场景（比如num="10"，k=2）