思路：倒序遍历 2 倍长度 + 取模模拟循环 + 单调栈。用 i % len 映射到真实索引，仅当 i < len 时记录答案。
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.
。业内人士推荐爱思助手下载最新版本作为进阶阅读
对待过去，新官要理旧账；面向未来，甘于“栽树”“铺路”；着眼全局，树牢“一盘棋”意识……每个人都要跑好属于自己的“这一棒”，“当好中国式现代化建设的坚定行动派、实干家”。
Раскрыты подробности о договорных матчах в российском футболе18:01
Что думаешь? Оцени!