一句话：提出 semantic flow 概念与 semantic flow graph，用 ML 内部状态增强控制流以支持动态分析。
基于机器学习的系统（如含 DNN 或 LLM 的软件）虽提供强大功能，但其内部行为对传统动态分析（如测试）大多不透明，现有分析通常只观察输入相似度或类别标签变化。本文提出 semantic flow，将控制流与执行中的内部状态（如 DNN 某层激活值、LLM agent 某推理步响应的 embedding）结合，形成 semantic flow graph，以刻画传统控制流未显式表示的内部决策。作者给出 DNN 与 LLM agent 两个示例，并概述该表示的性质以及如何借此将现有动态分析技术适配到 ML 软件系统。
ML-based systems are software systems that incorporates machine learning components such as Deep Neural Networks (DNNs) or Large Language Models (LLMs). While such systems enable advanced features such as high performance computer vision, natural language processing, and code generation, their internal behaviour remain largely opaque to traditional dynamic analysis such as testing: existing analysis typically concern only what is observable from the outside, such as input similarity or class label changes. We propose semantic flow, a concept designed to capture the internal behaviour of ML-based system and to provide a platform for traditional dynamic analysis techniques to be adapted to. Semantic flow combines the idea of control flow with internal states taken from executions of ML-based systems, such as activation values of a specific layer in a DNN, or embeddings of LLM responses at a specific inference step of LLM agents. The resulting representation, summarised as semantic flow graphs, can capture internal decisions that are not explicitly represented in the traditional control flow of ML-based systems. We propose the idea of semantic flow, introduce two examples using a DNN and an LLM agent, and finally sketch its properties and how it can be used to adapt existing dynamic analysis techniques for use in ML-based software systems.