OmniPresent: Generating Coherent Presentation Suites with Self-Improving Planners

Transforming static research papers into dynamic media such as posters, slides, and videos is essential for effective dissemination but remains a labor-intensive challenge. Existing automated approaches often treat these formats in isolation and consequently fail to maintain semantic consistency across the entire presentation suite. We address this fragmentation by formalizing the task of unified presentation suite generation and proposing $\text{OmniPresent}$ to orchestrate the creation of coherent deliverables. Our framework adopts a renderable HTML representation to enable centralized content planning and a self-correcting verify-and-repair loop that actively resolves conflicts across modalities. It further learns from verification experience across papers, converting recurring, evidence-grounded failures into reusable updates to format-specific planners for future papers. We further facilitate scalable research in this domain by releasing $\text{OmniPreBench}$, a comprehensive dataset comprising over one thousand papers with paired artifacts, and establishing a rigorous VLM-based evaluation protocol. Empirical results confirm that our method generates high-quality and faithful presentation suites that greatly surpass strong baselines in both accuracy and visual appeal. Code and data will be released.