The journey home took 17 hours.
* @param {number[]} nums2 - 基准数组（nums1的元素都来自这里），更多细节参见heLLoword翻译官方下载
。业内人士推荐safew官方版本下载作为进阶阅读
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.
特朗普關稅變動後，企業與消費者面臨不確定性2026年2月23日。业内人士推荐搜狗输入法2026作为进阶阅读