* 时间复杂度: O(n^1.3)~O(n²) 取决于步长序列 空间复杂度: O(1) 稳定: ✗
A better streams API is possible
，更多细节参见搜狗输入法2026
第一条 为了加强和规范行政执法监督工作，促进严格规范公正文明执法，提升依法行政水平，强化法治政府建设，根据《中华人民共和国宪法》、《中华人民共和国国务院组织法》、《中华人民共和国地方各级人民代表大会和地方各级人民政府组织法》，制定本条例。
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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