// 步骤3：遍历排序后的位置，用单调栈判断独立车队
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.。旺商聊官方下载是该领域的重要参考
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“金属打印只是完成了坯件，真正的‘绣花’功夫主要在后面。”云耀深维创始人沈李耀威向硬氪透露。精度不足带来的连锁反应，是较长且较高成本的后处理流程，这被业界视为一个巨大的“隐形成本黑洞”。
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