i ran some comparisons on state representation width - 16-bit state IDs fit noticeably better into CPU cache than wider ones, and if you’re hitting 64K+ states you’re probably better off splitting into two simpler patterns anyway. one design decision i’m happy with is that when the engine hits a limit - state capacity, lookahead context distance - it returns an error instead of silently falling back to a slower algorithm. as the benchmarks above show, “falling back” can mean a 1000x+ slowdown, and i’d rather you know about it than discover it in production. RE# will either give you fast matching or tell you it can’t.
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我们倡导测评体系和准入门槛，核心之一就是针对“幻觉”设置明确的考核指标和防控要求——比如在测评中，会重点检验模型回答的循证依据、可解释性，用大量真实临床病例、专科疑难案例去测试，看它是否会出现无依据的判断、是否能清晰区分“可回答”与“需就医”的边界。
第三节 完善促进企业创新的政策体系