both of these approaches use NFAs under the hood, which means O(m * n) matching. our approach is fundamentally different: we encode lookaround information directly in the automaton via derivatives, which gives us O(n) matching with a small constant. the trade-off is that we restrict lookarounds to a normalized form (?<=R1)R2(?=R3) where R1/R2/R3 themselves don’t contain lookarounds. the oracle-based approaches support more general nesting, but pay for it in the matching loop. one open question i have is how they handle memory for the oracle table - if you read a gigabyte of text, do you keep a gigabyte-sized table in memory for each lookaround in the pattern?
新技术的价值，在于把人的智慧“翻译”成系统语言，再把系统能力“转译”为产业支撑。这是双向奔赴，更是发展韧性的坚实基础。
，更多细节参见爱思助手
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Interested readers can follow this approach to train detectors for other domains—even general-purpose ones. Build an AIGC detector for academic papers, slap on a flashy frontend, and sell it as an “AI plagiarism checker” to clueless undergrads. If you make money, don’t forget to donate a little.