Appendix C Limitations and Future Work Limitations. Evaluation and human factors. The delivery scoreboard provides fine-grained, repeatable assessment, yet automatic metrics may not fully reflect audience perception (e.g., engagement, trust, and cognitive load) in real talks. Future work includes larger-scale user studies spanning speakers with diverse expertise and rehearsal practices. Scope of effects and robustness. Attention augmentation currently focuses on lightweight, controllable effects. More expressive effects and richer multimodal assets may further improve delivery, but also raise stability risks. Future. A promising direction is to adapt the underlying models to the delivery objective. Supervised finetuning can improve the consistency of logical chains and slide–script alignment under domain-specific styles, while reinforcement learning can optimize delivery rewards from the Delivery Scoreboard (e.g., SD and its pacing/coherence dimensions) and rehearsal-time user feedback. Such learning-based policies may further enhance personalization (speaker speed, audience profile) while preserving the controllability and safety constraints required in presentation settings. Appendix E Related Work We review automatic presentation generation and its evaluation, surveying agents from early systems to LLM frameworks, and expose the gap between current metrics and the need for holistic, audience-centric, delivery-oriented assessment. E.1 Existing Slide Agents Early academic systems. Early systems distilled scientific documents into slides via content extraction. DOC2PPT [ 29 ] introduced a hierarchical seq-to-seq model and a 6k document-slide benchmark. SlideSpawn [ 30 ] refined selection by PDF-to-XML conversion, salience-driven ML ranking, and ILP-based sentence picking. Both prioritized summarization over design coherence or audience tailoring. LLM-based multi-agent frameworks. Leveraging large language models, recent multi-agent frameworks automate presentation generation. PASS [ 18 ] pioneers a Word-to-slide pipeline with synchronized speech synthesis. Auto-Slides [ 31 ] enables dialog-driven customization, enhancing controllability. PPTAgent [ 16 ] refines slides via two-stage, edit-based generation guided by reference patterns, yielding superior content, aesthetics, and coherence. PreGenie [ 19 ] iteratively optimizes multimodal slides through analysis–generation–review cycles, ensuring aesthetic and semantic consistency. Editing-centric and interaction-focused agents. Beyond end-to-end slide generation, recent work studies instruction-following edits over existing decks. PPTArena [ 22 ] benchmarks natural-language slide editing and introduces PPTPilot, which improves controllability via structure-aware planning and verification. PresentAgent [ 32 ] further extends the setting to multimodal presentation video generation. Audience-aware and narrative-focused approaches. More closely related are methods that model audience and narrative structure. Persona-aware D2S [ 5 ] introduces audience expertise and duration as control variables, but its binary modeling is coarse for fine-grained background and time constraints. For narrative structure, NarrativeNet Weaver [ 33 ] uses hybrid vector–graph retrieval to maintain entity consistency and facilitate narrative chains, and He et al. [ 34 ] outline a four-phase framework (Data, Narration, Visualization, Presentation) with LLM-assisted narration tasks. User-centric systems. Additionally, industrial systems (Microsoft Copilot [ 35 ] , Google Gemini [ 36 ] , Beautiful.ai [ 37 ] and Gamma [ 12 ] ) enable easy generation. However, although these products can produce visually appealing slides, they often overlook control over presentation pacing and narrative style, and have yet to consider reducing users’ preparation burden from the perspective of the complete presentation workflow. E.2 Evaluation for AI-Generated Presentations Traditional text-based metrics. Early studies primarily reused summarization metrics, such as ROUGE [ 38 ] and BLEU [ 39 ] , to measure lexical overlap between slide text and sources. Later work adopted embedding-based metrics (e.g., BERTScore [ 40 ] and MoverScore) to better capture semantics. However, text-only metrics ignore visual design, layout structure, and text–image consistency. Multi-dimensional evaluation frameworks. To more comprehensively evaluate slide quality, researchers have proposed multi-dimensional evaluation frameworks. PASS [ 18 ] introduced LLM-based evaluation metrics, assessing presentations from three key dimensions: relevance, coherence, and redundancy. PPTAgent [ 16 ] proposed the PPTEval framework, which evaluates presentation quality comprehensively from content, design, and coherence dimensions, significantly surpass traditional single-metric evaluations. PreGenie [ 19 ] combines text-image relevance (CLIP Score) and figure proportion metrics to ensure multimodal consistency. Knowledge-Centric Templatic Views (KCTV) [ 20 ] proposed a template-agnostic evaluation framework TAE, adopting a Precision-Recall style that better aligns with human preferences. Task-specific benchmarks. Recent benchmarks target specific capabilities such as generation, editing, and visual reasoning. SlidesGen-Bench [ 21 ] evaluates content, aesthetics, and editability with Slides-Align1.5k, while PPTC Benchmark [ 41 ] measures multi-turn task completion via turn/session accuracy. PPTArena [ 22 ] focuses on instruction-following edits with dual VLM-as-judge scoring, and PPTBench [ 23 ] probes layout/design understanding across detection, understanding, modification, and generation. Human evaluation and LLM-as-a-judge. Human studies remain important for assessing slide quality, often along axes such as informativeness, persona fit, duration suitability, and coherence [ 29 ] . More recently, LLM-as-a-judge has been adopted for scalable evaluation; PPTAgent [ 16 ] reports alignment between MLLM judging and human ratings, and REFLEX [ 42 ] proposes reference-free judging via negative-sample fine-tuning to produce actionable feedback.
Appendix F Reproducibility Statement We will release the source code, configuration files, prompts, and evaluation scripts for reproducing the full pipeline (planning, retrieval, rendering, sandbox validation, and scoreboard evaluation) at: https://github.com/PUITAR/DeepSlide .