SciSlide: Scientific Slide Generation as Verifiable Argument Reconstruction

Preparing slides for a scientific talk is a document-to-multimodal generation task: text, figures, and equations must be placed on a visual canvas to guide an audience through the paper's causal argument. We formulate this task as argument reconstruction rather than extractive summarization, with explicit verification at each step. SciSlide builds a multi-slide argument graph in which every node carries a verbatim source span and every figure attaches to its supporting claim, enabling string-based verification of coverage and grounding. We score decks with sixteen computable metrics that operationalize the assertion–evidence paradigm for technical presentations, and learn only the layout component (coordinate prediction and geometric composition) from 7,500 conference decks paired with their papers. Against four published systems and a prompted frontier LLM, SciSlide leads on assertion headlines, argument-driven slide order, and figure–claim alignment; human raters prefer its decks in 65% of pairwise comparisons. For layout, autoregressive coordinate generation outperforms classification by 6.2 percentage points in strict exact match.