SlideLab: Audience-Centered Scientific Slide Generation and Evaluation

Abstract:Scientific presentations are more than summaries of research papers. They need to present the work in a coherent sequence, explain the main ideas clearly, and help the audience follow the presentation. We present SlideLab, a training-free multi-agent framework for generating scientific presentations from research papers. SlideLab first plans the presentation narrative, then builds and iteratively refines a shared slide deck using agents for content planning, visual generation, layout refinement, and grounding verification. In a blind human preference study, SlideLab was preferred over both open-source and commercial systems on 77% of papers while using roughly 4 times fewer inference tokens than the strongest open-source baseline. We also introduce ConfArena, an audience-oriented evaluation framework that simulates a conference room and assesses presentations slide by slide. ConfArena matches human system rankings and detects injected presentation problems, including falsified numbers, degraded figures, dropped slides, and shuffled slide order.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2609.30294 [cs.CL]

(or arXiv:2609.30294v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.30294 Focus to learn more arXiv-issued DOI via DataCite

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