arXiv AI
ConvDeck: Conversational Paper-to-Slide Generation via Stage-Specific User Feedback
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arXiv AI
Aug 25
SlideGen: Collaborative Multimodal Agents for Scientific Slide Generation
SlideGen is a collaborative vision‑language multi‑agent framework designed to generate scientific presentation slides from research papers. It assigns specialized agents to outline the presentation structure, align figures and tables with key claims, generate speaker notes, and compose editable PPTX slides using a diverse layout library. The system introduces a geometry‑aware density metric to evaluate visual clutter and demonstrates significant improvements in layout balance, content coverage, and text coherence over existing baselines on a 200‑paper benchmark.
Hugging Face Trending Papers
Sep 2
SLIDEFORGE: An LLM Agent for Controllable Editing of Slides as Structured Artifacts
SLIDEFORGE is an LLM‑driven agent designed for controllable editing of slide decks while preserving layout, style, component structure, and native editability. It constructs a Deck State Graph that links visual decomposition, PowerPoint object structure, and perceptual organization, enabling theme‑preserving reconstruction through slide‑native operations and rendered‑state verification. The authors also propose a comprehensive evaluation framework measuring component recovery, preservation, restyling consistency, visual quality, and native editability, and demonstrate that SLIDEFORGE outperforms direct prompting, screenshot‑based agents, and generic code‑agent baselines.
arXiv AI
Jun 30
Semantic Prompting: Agentic Incremental Narrative Refinement through Spatial Semantic Interaction
arXiv:2604. 19971v2 Announce Type: replace-cross Abstract: Interactive spatial layouts empower users to synthesize information and organize findings for sensemaking.
arXiv Computer Vision
Sep 4
SLIDEFORGE: An LLM Agent for Controllable Editing of Slides as Structured Artifacts
SLIDEFORGE is a new AI agent designed for controllable editing of presentation slides. It constructs a Deck State Graph that links visual decomposition, native PowerPoint object structure, and perceptual organization, enabling theme‑preserving reconstruction through slide‑native operations and rendered‑state verification. The authors also propose an evaluation framework that jointly measures component recovery, preservation, restyling consistency, visual quality, and native editability, and demonstrate that SLIDEFORGE outperforms existing prompting, screenshot‑based, and generic code‑agent baselines.
arXiv Computation and Language
Sep 1
PaperBanana-Interact: Scientific Diagram Refinement with Multi-Turn Human Feedback
PaperBanana-Interact is a multi-agent system designed to refine scientific diagrams through multi-turn human feedback. The authors introduce MTPaperBananaBench, a benchmark with 292 images and 3,518 user requirements, and a user simulator that generates natural language feedback at each turn. Experiments show that PaperBanana-Interact consistently improves diagram quality, outperforming baseline systems by 11.9–18.6 points and reducing forgetting by 3.7–6.2 points.
arXiv AI
Sep 2
ReDeck: Step-Level Render-Grounded Refinement for Document-to-Slide Generation
ReDeck introduces a step‑level render‑grounded refinement framework for document‑to‑slide generation, breaking slide revision into atomic edit actions with immediate renderer‑derived observations. It employs multi‑granular feedback—step‑level spatial checks, turn‑level adaptive critique, and a submission‑level layout gate—to balance local repair with overall quality. The authors also present DeckQuiz, a benchmark that separates content fidelity, spatial correctness, and design quality, and demonstrate ReDeck’s superior performance across GPT‑5.4, Claude‑4.6, and Gemini‑3.1.