arXiv AI
ConvDeck: Conversational Paper-to-Slide Generation via Stage-Specific User Feedback
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
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.
arXiv Computer Vision
Oct 21
Learn to Modify: Self-evolving Multi-agent Refinement Framework for Conversational Text-to-Motion Generation
The paper introduces SMART, a Self‑evolving Multi‑Agent Refinement Framework for conversational text‑to‑motion generation. It enables users to iteratively refine specific spatio‑temporal regions of a motion sequence based on natural language feedback, preserving the rest of the motion. The framework incorporates a semantic‑guided masked editing mechanism and a memory‑based self‑evolving strategy to reuse past editing knowledge and reduce redundant exploration.
arXiv AI
Sep 28
SlideLab: Audience-Centered Scientific Slide Generation and Evaluation
SlideLab is a training‑free, multi‑agent framework that generates scientific presentations directly from research papers. It first plans a coherent narrative, then iteratively builds and refines a shared slide deck using agents for content planning, visual generation, layout refinement, and grounding verification. In a blind human preference study, SlideLab outperformed both open‑source and commercial systems on 77% of papers while using about four times fewer inference tokens than the strongest open‑source baseline. The authors also introduce ConfArena, an audience‑oriented evaluation framework that simulates a conference room and assesses presentations slide by slide, matching human system rankings and detecting issues such as falsified numbers, degraded figures, dropped slides, and shuffled slide order.
arXiv Computer Vision
Oct 27
One Sentence, One Drama: Personalized Short-Form Drama Generation via Multi-Agent Systems
arXiv:2605.22144v2 Announce Type: replace Abstract: Existing approaches for digital short-drama production typically rely on one-shot LLM generated scripts and loosely coupled pipelines, which fail t...
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.