对于 OpenClaw 来说，第一步（查票）做得很惊艳，它能像真人一样操作网页。但到了第二步和第三步往往会陷入“半拉子”状态——因为现实企业系统并不像公开网页那样标准化：
Получивший взятку в размере 180 миллионов экс-мэр российского города обратился к суду14:53
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OpenAI总裁Greg Brockman为特朗普的政治献金，成为了众矢之的。
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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?