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底层通过状态机逐字符处理模板字面片段,实时追踪解析状态:标签内、属性名、属性值(区分类型)、注释段、脚本块、样式块或纯文本区域。当模板遍历器遇到变量时,状态机已明确其上下文,自动应用相应转义策略:
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The implications are no longer just a “fear”. In July 2025, Replit’s AI agent deleted a production database containing data for 1,200+ executives, then fabricated 4,000 fictional users to mask the deletion.
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Although comparing crash rates boils down to 4 simple counts – crashes and miles for Automated Driving System (ADS) and a benchmark – there are many decisions about the study design and data sources used that can affect the outcome. Safety impact research has been a well-used tool in the vehicle safety research literature, dating back to safety advances like electronic stability control and automated emergency braking. ADS which are responsible for the entire dynamic driving task present some unique challenges, and as a result the RAVE Checklist was published as a consensus of research best practices for ADS safety impact research. The checklist, which is being developed into an international standard, lays out the best practices for conducting safety impact studies of ADS like presented on the Safety Impact Data Hub. The research that underpins the safety impact data hub is designed to comply with the RAVE Checklist (see the online appendix of Kusano et al., 2025, for a conformance assessment of the methods against the RAVE checklist requirements).
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