提出可确定性封装生成模型的四大原则，降低AI集成风险。
Grounded Inference: Principles for Deterministically Encapsulated Generative Models
- 定义四种生成模型融合架构的底层原语
- 揭示行业中普遍存在的两类反模式陷阱
- 为系统集成与模型接口设计提供安全框架
将生成模型融入传统计算系统既带来巨大机遇，也伴随严重风险。尽管许多早期采用者已为此付出高昂代价，该领域仍缺乏基础性框架来降低整合AI的风险。本文通过定义四种特定的AI混合架构原语，建立确定性封装概率模型的基础。同时，识别出行业普遍存在的两类反模式，作为工程师的警示。该框架旨在促进AI在传统系统中的成功集成，并为生成模型供应商构建下一代模型接口提供基础。
原文摘要 · Abstract (English)
The incorporation of generative models into traditional computational systems presents both enormous opportunity and tremendous peril. Although many early adopters have realized these perils at great expense, the field still requires foundational frameworks to de-risk incorporation of AI into traditional systems. This manuscript establishes this foundation through the definition of four specific primitives of AI blended architecture, designed to enable deterministic encapsulation of probabilistic models. It further establishes two overarching anti-patterns broadly represented across industry to serve as warnings for engineers in this field. This framework was designed to enable successful integration of AI into traditional systems while providing a foundation upon which generative model providers could build the next generation of generative model interfaces.
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