---
id: 20260815-T0-02
title: "路径积分统一生成模型：流、扩散、GAN 殊途同归"
title_en: "Path Integral Unifies Flow, Diffusion, and Adversarial Models"
url: https://ai.daily.yangsir.net/daily/20260815-T0-02
issue_date: 2026-08-15
publish_date: 2026-08-14T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2608.12438
---

# 路径积分统一生成模型：流、扩散、GAN 殊途同归

arXiv 新论文提出将生成式建模统一为路径积分框架。在该框架下，基于流的、基于扩散的、变分和对抗生成模型，都被视为同一个主作用量的不同评估方式。论文利用 Martin-Siggia-Rose-Janssen-de-Dominicis 形式，为理解不同生成模型的共性提供了统一理论视角。这一发现可能为设计新一代更高效、更通用的生成式 AI 模型提供指导。

## English Version

**Path Integral Unifies Flow, Diffusion, and Adversarial Models**

A new arXiv paper proposes unifying generative modeling as a path integral, in which flow-based, diffusion-based, variational, and adversarial models arise as different evaluation principles for a single master action. Using the Martin-Siggia-Rose-Janssen-de-Dominicis (MSRJD) formalism, the paper offers a unified theoretical perspective on the commonalities of different generative models. This finding could guide the design of a new generation of more efficient and versatile generative AI models.

---

**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2608.12438)

**详情页**：https://ai.daily.yangsir.net/daily/20260815-T0-02

---

*智语观潮 · Daily — https://ai.daily.yangsir.net/llms.txt*