用图形化概率模型分析大模型软件中的生成流程。
Graphical-Probabilistic Modeling of Generative Flows in LLM-Native Software Systems
- 提出生成网络，用图形化概率模型描述大模型的生成过程。
- 能捕捉大模型的随机性和提示依赖性，支持系统级分析。
- 适合需要可解释、可验证的大模型系统设计者。
构建大模型原生软件仍是一个充满挑战且不成熟的领域。当前实践多依赖实验和启发式方法（如提示工程、上下文设计），这些方法层次低，缺乏支持设计层面推理与分析的结构化框架。相比之下，传统软件工程通过模块化和抽象来表达和分析系统行为。为将这种严谨性引入大模型原生开发，本文提出记录生成流并陈述大模型软件设计属性的方法。这些方法需兼顾大模型固有的随机性与提示依赖性，同时具备足够表达力以捕捉涌现现象。初步方案基于定制化的图形化概率模型，旨在构建一个可对大模型中心架构中的生成交互与系统级特性进行严谨推理的基础框架。
原文摘要 · Abstract (English)
Engineering LLM-native software remains a challenging and immature field. Current practice is largely exploratory, relying on experimentation and heuristic techniques such as prompting and context engineering. These, however, are low-level and lack the principled structure needed to support design-level reasoning or analysis. In contrast, traditional software engineering leverages modularity and abstraction to communicate and analyze system behavior. To bring similar rigor to LLM-native development, we propose methods for documenting generative flows and for stating properties of LLM-based software designs. Such methods must account for the stochastic, prompt-dependent behavior of large language models while remaining expressive enough to capture emergent phenomena. Our initial approach is based on graphical probabilistic models, tailored to capture phenomena characteristic of LLM-native systems. This framework -- what we term Generation Networks -- aims to provide a foundation for principled reasoning about generative interactions and system-level properties in LLM-centric software architectures.
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