Lazy DFAs (2010) are a clever optimization to mitigate the O(2^m) blowup of DFA construction, by only constructing the states that you actually visit. lazy DFAs reduce the theoretical automata construction time to either O(2^m) or O(n), whichever is lower. you could argue that it’s theoretically no longer linear time, since you could have a regex that creates a new state for every character in the input, but in practice you will keep revisiting the same states. for all intents and purposes it behaves more like O(n) with some initial wind-up time. the main downside of lazy DFAs is that they are more complex to implement, and you have to ship a compiler as part of your regex algorithm. i want to highlight Rust regex and RE2 as excellent implementations of this approach, which you can also see in the benchmarks.
«Кто не боится работать, голодным здесь не останется»История девушки, которая в погоне за мечтой переехала в США12 октября 2020
。关于这个话题，PDF资料提供了深入分析
值得注意的是，阿里在去年9月发布的Qwen3-Max Preview曾一度进入该榜单的前三，与Gemini、Claude、ChatGPT等全球顶尖闭源模型比肩，但随着各家AI厂商在去年年末和今年年初更新大模型版本，Qwen从第一梯队头部持续下滑，Qwen3.5的发布并没有扭转这一势头。
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