SLIDEFORGE: An LLM Agent for Controllable Editing of Slides as Structured Artifacts

2026-09-02

AI Summary

为解决AI辅助幻灯片编辑中布局和可编辑性保留的问题，提出SLIDEFORGE框架，通过构建Deck State Graph保持结构与风格，支持可控编辑。

Abstract

Current AI agents compellingly describe slides. However, AI-assisted slide editing requires more than understanding: the output must retain layout, style, component structure, and native editability. Towards, AI-assisted slide editing, existing agents operate on screenshots or weak document representations and often fragment coherent visual units, rasterize editable content, or break layout. In contrast, for controllable slide editing, we introduce an agentic framework, SLIDEFORGE, which builds a Deck State Graph, an executable slide state that links visual decomposition, native pptx object structure, and perceptual organization. By recovering human-referable components while retaining fine-grained editable structure, SLIDEFORGE supports theme-preserving reconstruction through slide-native operations and rendered-state verification. We further introduce an evaluation paradigm for controllable slide transformation that jointly measures component recovery, preservation, restyling consistency, visual quality, and native editability. Experiments show that SLIDEFORGE outperforms direct prompting, screenshot-based agents, and generic code-agent baselines across these dimensions. Code is available at https://github.com/UIUC-MONET/SLIDEFORGE.

Problem

• AI-assisted slide editing
• layout
• editable content
• visual units
• component structure

Innovation

• controllable slide editing
• Deck State Graph
• native editability
• theme-preserving reconstruction