DeepSlide: From Artifacts to Presentation Delivery
DeepSlide is a human-in-the-loop multi-agent system that optimizes the entire presentation process—from requirement elicitation and narrative planning to slide-script generation and rehearsal support. It introduces a dual-scoreboard benchmark separating artifact quality from delivery excellence, achieving gains in narrative flow, pacing, and slide-script synergy across 20 domains.
[2605.15202] DeepSlide: From Artifacts to Presentation Delivery
[Submitted on 1 Apr 2026]
Title:DeepSlide: From Artifacts to Presentation Delivery
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Abstract:Presentations are a primary medium for scholarly communication, yet most AI slide generators optimize the artifact (a visually plausible deck) while under-optimizing the delivery process (pacing, narrative, and presentation preparation). We present DeepSlide, a human-in-the-loop multi-agent system that supports preparing the full presentation process, from requirement elicitation and time-budgeted narrative planning, to evidence-grounded slide--script generation, attention augmentation, and rehearsal support. DeepSlide integrates (i) a controllable logical-chain planner with per-node time budgets, (ii) a lightweight content-tree retriever for grounding, (iii) Markov-style sequential rendering with style inheritance, and (iv) sandboxed execution with minimal repair to ensure renderability. We further introduce a dual-scoreboard benchmark that cleanly separates static artifact quality from dynamic delivery excellence. Across 20 domains and diverse audience profiles, DeepSlide matches strong baselines on artifact quality while consistently achieving larger gains on delivery metrics, improving narrative flow, pacing precision, and slide--script synergy with clearer attention guidance.
Comments: 37 pages,10 figures,9 tables
Subjects:
Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR)
ACM classes: I.2.7
Cite as: arXiv:2605.15202 [cs.AI]
(or arXiv:2605.15202v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.15202
arXiv-issued DOI via DataCite
Submission history
From: Zhiwei Zhang [view email] [v1] Wed, 1 Apr 2026 13:38:36 UTC (9,787 KB)
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