ReDeck：面向文档到幻灯片生成的步骤级渲染驱动的优化方法

中文摘要 AI 辅助

文档到幻灯片生成颇具挑战性，因为幻灯片是密集的可编辑产物，既需要忠实的内容选择，也需要精确的空间布局。近期的幻灯片智能体采用迭代反思机制，但通常遵循整体式的“一个版本，一次反馈”循环：生成单张幻灯片或整套幻灯片后进行重写，仅在轮次边界处进行评判。这种延迟反馈使得溢出、重叠、裁剪、画布外放置等局部错误难以归因和修复。我们提出ReDeck，这是一个步骤级渲染驱动的优化框架，它将幻灯片修订分解为原子编辑动作，并在每一步返回渲染器生成的观测结果，将优化转变为“一次编辑，一次观测”。为平衡局部修复与全局质量，ReDeck采用多粒度反馈：用于空间错误的步骤级渲染反馈、用于语义和设计指导的轮次级自适应评判器，以及用于严格布局验证的提交级闸门。我们还推出DeckQuiz基准，该基准将内容保真度、空间正确性和设计质量解耦。在GPT-5.4、Claude-4.6和Gemini-3.1模型上，ReDeck的性能始终优于现有幻灯片生成智能体， ablation实验（ ablation即消融实验）证实反馈时机和粒度对可靠的幻灯片优化至关重要。

英文摘要

Document-to-slide generation is challenging because slides are dense editable artifacts that require both faithful content selection and precise spatial layout. Recent slide agents adopt iterative reflection, but typically follow a monolithic "one version, one feedback" loop: a slide or deck is rewritten, rendered afterward, and critiqued only at the turn boundary. This delayed feedback makes local failures such as overflow, overlap, clipping, and off-canvas placement difficult to attribute and repair. We propose ReDeck, a step-level render-grounded refinement framework that decomposes slide revision into atomic edit actions and returns renderer-derived observations after each step, turning refinement into "one edit, one observation." To balance local repair with global quality, ReDeck uses multi-granular feedback: step-level render feedback for spatial errors, a turn-level adaptive critic for semantic and design guidance, and a submission-level gate for hard layout validation. We further introduce DeckQuiz, a benchmark that decouples content fidelity, spatial correctness, and design quality. Across GPT-5.4, Claude-4.6, and Gemini-3.1, ReDeck consistently outperforms existing slide-generation agents, and ablations confirm that feedback timing and granularity are critical for reliable slide refinement.

发表机构

• Microsoft Corporation（微软公司）
• Shanghai Jiao Tong University（上海交通大学）
• Fudan University（复旦大学）

机构由 AI 辅助整理，请以论文原文为准。