ReDeck: Step-Level Render-Grounded Refinement for Document-to-Slide Generation

arXiv:2609.00194 · cs.AI · Submitted 2026-08-31 · Read on arXiv

cs.AI

Submitted: 2026-08-31

Updated: 2026-09-05

Code: https://github.com/microsoft/ReDeck

Project page: https://aka.ms/ReDeck

Terminology

• Benchmark — 23× in this paper · explained in 2 episodes
• Fidelity — 23× in this paper · explained in 2 episodes
• Ablation — 15× in this paper · explained in 1 episode
• Alignment — 10× in this paper · explained in 1 episode
• Action Space — 6× in this paper · explained in 1 episode
• Calibration — 6× in this paper · explained in 7 episodes
• Consistency — 5× in this paper · explained in 3 episodes
• Grounding — 5× in this paper · explained in 3 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Adaptive Scheduler — 3× in this paper · explained in 1 episode
• Benchmarking — 3× in this paper · explained in 3 episodes
• Conference on Computer Vision and Pattern Recognition — 3× in this paper
• In International Conference on Learning Representations — 3× in this paper
• LLM-as-a-Judge — 3× in this paper · explained in 3 episodes
• Verification — 3× in this paper · explained in 2 episodes
• Chain-of-Thought Prompting — 2× in this paper · explained in 2 episodes
• Iterative Refinement — 2× in this paper · explained in 3 episodes
• Provenance — 2× in this paper · explained in 3 episodes
• Scaffolding — 2× in this paper · explained in 3 episodes
• Scaling Laws — 2× in this paper · explained in 4 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Core — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Empirical Methods in Natural Language Processing — 1× in this paper
• External Validation — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• In International Conference on Machine Learning — 1× in this paper
• Meeting of the Association for Computational Linguistics — 1× in this paper
• Model Drift — 1× in this paper · explained in 4 episodes
• Multi-Agent Framework — 1× in this paper · explained in 3 episodes
• Multi-Agent System — 1× in this paper · explained in 5 episodes
• Personalization — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Reward Hacking — 1× in this paper · explained in 8 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Verifiable Rewards — 1× in this paper · explained in 2 episodes

Sources

• DOC2PPT: Automatic Presentation Slides Generation from Scientific Documents
• PPTAgent: Generating and Evaluating Presentations Beyond Text-to-Slides
• SlideGen: Collaborative Multimodal Agents for Scientific Slide Generation
• DeepPresenter: Environment-Grounded Reflection for Agentic Presentation Generation
• Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
• ReAct: Synergizing Reasoning and Acting in Language Models
• Enhancing Presentation Slide Generation by LLMs with a Multi-Staged End-to-End Approach
• Auto-Slides: An Interactive Multi-Agent System for Creating and Customizing Research Presentations
• SlideTailor: Personalized Presentation Slide Generation for Scientific Papers
• Narrative-Driven Paper-to-Slide Generation via ArcDeck
• Design2Code: Benchmarking Multimodal Code Generation for Automated Front-End Engineering
• Unlocking the conversion of Web Screenshots into HTML Code with the WebSight Dataset
• LayoutTransformer: Layout Generation and Completion with Self-attention
• LayoutDM: Discrete Diffusion Model for Controllable Layout Generation
• LayoutGPT: Compositional Visual Planning and Generation with Large Language Models
• Teaching Large Language Models to Self-Debug
• AgentCoder: Multi-Agent-based Code Generation with Iterative Testing and Optimisation
• MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework
• AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
• SWE-bench: Can Language Models Resolve Real-World GitHub Issues?

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