Problem
Research questions and friction points this paper is trying to address.
Document-to-slide generation
spatial layout
iterative reflection
delayed feedback
local failures
ReDeck: Step-Level Render-Grounded Refinement for Document-to-Slide Generation
🤖 AI Summary
为解决文档转幻灯片时的内容选择和布局问题，提出ReDeck框架，通过逐步渲染反馈进行局部修正，并结合多层级反馈确保全局质量。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
step-level render-grounded refinement
multi-granular feedback
atomic edit actions
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