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InfoVis: Unified Slide Image Generation and Editing
Jiahao Wang, Xiyan Jiang, Chenyuan Wu, Haoqiang Zhang, Xin Luo, Yueze Wang, Defu Lian, Bo Zhang, Jiajun Zhang, Zheng Liu
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Abstract
Abstract:Generating and editing presentation slides requires models to combine accurate text rendering, instruction following, visual grounding, aesthetic design, and precise editing within one canvas. Existing work covers only subsets of these capabilities and lacks large-scale slide-native supervision and diagnostic evaluation. We formulate four slide-centric tasks and build a scalable data engine with over 13M continual-pretraining records and 1.9M supervised fine-tuning records. We also introduce VisSlideBench, a benchmark of 1,527 manually screened instances that covers all four tasks with task-specific, multidimensional evaluation. Its automatic rankings agree with blinded, dimension-wise human rankings. Using these resources, we train InfoVis, a unified 9B slide generation and editing model. On VisSlideBench, InfoVis leads every evaluated open-source model on all eleven metrics and scores 7.94 overall against 7.99 for Nano-Banana-2. Starting from FLUX.2-klein-base-9B, slide-specific post-training adds 1.6 points overall, with the largest generation gains in content faithfulness; the remaining gap to proprietary models is smallest in content faithfulness and largest in aesthetic quality. Built on InfoVis, our document-to-slide workflow achieves the second-highest Aesthetics score on the external SlidesGen-Bench. We will release the model, code, data, and benchmark.
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- Source: https://arxiv.org/abs/2610.16784v1
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Preprint INFOVIS: UNIFIED SLIDE IMAGE GENERATION AND EDITING Jiahao Wang 1,2,∗ Xiyan Jiang 2,3,∗ Chenyuan Wu 4,∗ Haoqiang Zhang 4,† Xin Luo 4 Yueze Wang 2 Defu Lian 4 Bo Zhang 3 Jiajun Zhang 1 Zheng Liu 2,‡ 1 Institute of Automation, Chinese Academy of Sciences 2 Beijing Academy of Artificial Intelligence 3 Zhejiang University 4 University of Science and Technology of China Code: https://github.com/VectorSpaceLab/InfoVis ABSTRACT Generating and editing presentation slides requires models to combine accurate text rendering, instruction following, visual grounding, aesthetic design, and pre- cise editing within one canvas. Existing work covers only subsets of these capa- bilities and lacks large-scale slide-native supervision and diagnostic evaluation. We formulate four slide-centric tasks and build a scalable data engine with over 13M continual-pretraining records and 1.9M supervised fine-tuning records. We also introduce VisSlideBench, a benchmark of 1,527 manually screened instances that covers all four tasks with task-specific, multidimensional evaluation. Its auto- matic rankings agree with blinded, dimension-wise human rankings. Using these resources, we train InfoVis, a unified 9B slide generation and editing model. On VisSlideBench, InfoVis leads every evaluated open-source model on all eleven metrics and scores 7.94 overall against 7.99 for Nano-Banana-2. Starting from FLUX.2-klein-base-9B, slide-specific post-training adds 1.6 points overall, with the largest generation gains in content faithfulness; the remaining gap to propri- etary models is smallest in content faithfulness and largest in aesthetic quality. Built on InfoVis, our document-to-slide workflow achieves the second-highest Aesthetics score on the external SlidesGen-Bench. We will release the model, code, data, and benchmark. 1INTRODUCTION Image generation has rapidly expanded beyond natural-image synthesis toward structured, text-rich visual content, including posters, advertisements, infographics, and presentation slides (Wang et al., 2025c; Peng et al., 2025; Gao et al., 2025; Chen et al., 2025; Hu et al., 2025). Slides are particularly demanding because capabilities often developed separately must work together reliably within a single canvas. Models must render text accurately, arrange heterogeneous elements into coherent layouts, incorporate supplied visual assets faithfully, and apply local or global edits while preserving unrelated content. Recent open-source image generators have made substantial progress (Black Forest Labs, 2025; Z-Image Team, 2025; Wu et al., 2025; Meituan LongCat Team et al., 2025), yet they still combine these capabilities unreliably. Accurate, polished, and controllable slide generation and editing therefore remain difficult. A major obstacle is the lack of slide-native supervision and diagnostic evaluation. General-purpose image generators are trained on broad visual distributions, with limited supervision for the content– layout–design relationships characteristic of presentation slides. Meanwhile, existing benchmarks primarily target programmatic or deck-level presentation generation, or include slides only as a subset of broader visual-content tasks (Ge et al., 2025; Huang et al., 2025; Li et al., 2026; Chen et al., 2026; Yang et al., 2026). These settings do not systematically isolate the capabilities required for single-slide image generation and editing. ∗ Equal contribution. † Core contributor. ‡ Corresponding author. 1 arXiv:2610.16784v1 [cs.CV] 12 Oct 2026 Preprint Create a pastel cartoon slide titled "GUIDE TO PRODUCTIVE LEARNING". Use three panels: "1. EFFECTIVE STUDY METHODS", "2. TIME MANAGEMENT TIPS", and "3. OPTIMAL LEARNING ENVIRONMENT". Add four practical tips per panel. Use cute stationery, a smiling clock, a sleeping cat, and a blue-peach backdrop. Text-to-slide Template-conditioned Multi-image grounded Slide editing Use both images to create "ANIMATING EMOTION: THE CREATIVE VISION". Combine the illustration and musical motif... Follow the reference style; create a remote-work slide...Restyle "Spring" with butterflies; keep all text... IMAGE GENERATOR InfoVis 1 · Slide-native CPT 2 · Four-task mixed CPT T2IRefMultiEdit 3 · Task SFT + fusion θ t2i θ ref θ multi θ edit Unified model VisSlideBench Generation Editing CF Content faithfulness IF Instruction following AQ Aesthetic quality evaluator SC Semantic consistency IF Instruction following EC Edit consistency PQ Presentation quality Qwen-Image / EditFLUX.2-devNano-Banana-2InfoVis Reference 4 6 10 5.51 6.49 7.60 7.70 Multi-image 4 6 10 4.47 6.39 7.78 7.81 T2I 4 6 10 5.93 6.38 7.89 7.68 Edit 4 6 10 7.57 6.63 8.83 8.41 8 88 8 Figure 1: Overview of our slide tasks, InfoVis, and VisSlideBench. InfoVis is trained through three stages on four generation and editing tasks, and VisSlideBench evaluates each task along task- specific dimensions. Bar values are the dimension means of Tables 12–15. In this work, we cast slide creation as a unified conditional visual synthesis problem and study four tasks under a shared text-and-image interface: text-to-slide generation, template-conditioned gener- ation, multi-image grounded generation, and instruction-based slide editing (Section 3.1). The tasks share requirements for accurate content rendering and coherent visual composition, while visual in- puts serve as style references, content assets, or source slides to edit, which motivates supporting all four within a unified model. Figure 1 summarizes our data, model, and benchmark. This shared view directly shapes our data design. We build a scalable slide-native data engine that combines real presentations with synthetic supervision and covers the conditioning requirements of all four tasks. Its instructions range from concise user requests to detailed descriptions and structured style_json specifications, so that one model serves both human-facing and machine-generated design conditions. Editing pairs come from deterministic PPTX transformations with instructions derived from the executed changes, complemented by model-generated targets for open-ended visual transformations. The resulting resource contains over 13M continual-pretraining records and 1.9M supervised fine-tuning records. To systematically diagnose slide generation and editing capabilities, we introduce VisSlideBench, a benchmark of 1,527 manually screened instances dedicated to single-slide image generation and editing across all four tasks. VisSlideBench measures complementary dimensions of slide quality. It evaluates generation in terms of Content Faithfulness, Instruction Following, and Aesthetic Qual- ity, and editing in terms of Semantic Consistency and Presentation Quality. The generation track spans the same three prompt settings as the training data. The automatic evaluator’s dimension-wise rankings agree with blinded human judgments. Using these resources, we train InfoVis, a unified 9B slide generation and editing model based on FLUX.2-klein-base-9B (Black Forest Labs, 2026). On VisSlideBench, InfoVis reaches an overall score of 7.94, leading every evaluated open-source model on all eleven metrics, against 7.99 for Nano-Banana-2 (Google, 2026). Our analysis yields three findings: the gains come from slide- specific supervision rather than scale, as InfoVis is trained from FLUX.2-klein-base-9B and out- performs the 32B FLUX.2-dev; unification costs little relative to our own task specialists, unlike existing unified open-source models, which trail specialists by a wide margin; and the remaining 2 Preprint gap to proprietary models is ordered by subjectivity, from content faithfulness through instruction following to aesthetic quality. Beyond VisSlideBench, InfoVis is the best open-source model in the BizGenEval Slides comparison (Li et al., 2026), and our document-to-slide workflow ranks second among ten systems in Aesthetics on SlidesGen-Bench (Yang et al., 2026). Our main contributions are: • We formulate four slide generation and editing tasks and build a slide-native data engine with over 13M continual-pretraining and 1.9M supervised fine-tuning records. • We introduce VisSlideBench, a benchmark for single-slide image generation and editing that covers all four tasks with task-specific, multidimensional evaluation. • We develop InfoVis, a unified 9B model that leads every evaluated open-source model on all eleven VisSlideBench metrics, scores 7.94 overall against 7.99 for Nano-Banana-2, and is the best open-source model on BizGenEval Slides. Built on InfoVis, our document-to-slide workflow ranks second among ten systems in Aesthetics on SlidesGen-Bench. 2RELATED WORK Structured Rich-Text and Design Image Generation Visual text generation methods improve text fidelity through specialized glyph, position, layout, and text-attribute representations (Ma et al., 2023; Chen et al., 2023; 2024; Tuo et al., 2024b;a; Shuai et al., 2026; Mao et al., 2026). Building on these advances, recent datasets and models extend text-aware generation to dense and paragraph- level scenarios (Wang et al., 2025b;a). Beyond rendering text accurately, another line of work studies the generation of complete design images, including posters, product advertisements, infographics, and slides (Wang et al., 2025c; Peng et al., 2025; Gao et al., 2025; Chen et al., 2025; Hu et al., 2025; Cui et al., 2026). Related work also studies factual consistency during structured visual generation and editing (Zhuo et al., 2026), emphasizing that visual quality must be coupled with faithful content realization. General-purpose generators (Black Forest Labs, 2025; Z-Image Team, 2025; Wu et al., 2025) and unified generation–editing models (Deng et al., 2025; Wu et al., 2026) handle diverse inputs but lack slide-native supervision, while the methods above typically optimize isolated visual artifacts and do not jointly address slide-specific template conditioning, multi-image grounding, and instruction-based editing. InfoVis unifies these capabilities in a slide-centric generator with shared content, layout, and design supervision. Benchmarks for Slide Generation Existing benchmarks evaluate slide generation from comple- mentary perspectives. SlidesBench (Ge et al., 2025) focuses on programmatic single-slide gen- eration, while EvoPresent (Liu et al., 2025) evaluates end-to-end paper-to-presentation systems. PPTBench (Huang et al., 2025) covers PowerPoint understanding, modification, and generation, whereas BizGenEval (Li et al., 2026) treats slides as one category of broader commercial visual con- tent. More recent benchmarks, including PresentBench (Chen et al., 2026), SlidesGen-Bench (Yang et al., 2026), and DECKBench (Jang et al., 2026), introduce fine-grained evaluation protocols for slide generation: SlidesGen-Bench separates content, aesthetic, and editability criteria for repro- ducibility, and DECKBench further extends evaluation to multi-turn slide editing. These bench- marks nevertheless remain centered on complete presentations and agentic workflows, and provide limited diagnosis of individual slide capabilities. VisSlideBench instead targets single-slide im- age generation and editing, evaluating text fidelity, template consistency, visual grounding, and edit preservation with task-specific protocols and metrics. 3TASK FORMULATION AND SLIDE DATA ENGINE Our slide data engine recovers slide content and visual elements from presentation pages and con- verts them into task-specific conditions while keeping the complete slide as the target; editing data instead comes from controlled changes to editable slides. The four tasks thus share content, layout, and design supervision. Appendix A gives statistics and implementation details. Figure 2 summa- rizes the construction. 3 Preprint A. Continual Pretraining Data Generation Visual Recognition E = mc² Academic talks Text, equations, tables & figures Climate Action Broader presentations Templates, colors & graphic styles Quality filtering Shared parsing 1 2 3 OCR reading order Formulas & tables Visual regions Renewable Energy Extracted assets Text + localized visual assets Instruction annotation Short requestDetailed descriptionStyle_json Text-to-slide Instruction “Renewable energy...” Renewable Energy Template Climate Action Instruction + adjacent ref. Renewable Energy Multi-image Instruction + assets Renewable Energy Same target slide, different conditioning inputs Editing Real editable PPTX + synthetic PPTX Execute PPTX edits Text · Add / remove block Background · Color · Font · Typography Render targetConstruct instructionPair checks Renewable EnergyRenewable Energy SourceTarget “Change the title color to red.” B. Supervised Fine-Tuning Data Generation LLM conceptsSlideShare contentPapers → slide specs T2I synthesis Slide content Nano- Banana-2 Ocean Conservation Template synthesis Curated template Style prompt Nano- Banana-2 Reference Climate Action Slide content Nano- Banana-2 Sustainable Cities Final instruction annotation: T2I & template pairs Multi-image from synthesized targets Sustainable Cities Synthesized target Extract assets Multi-image pair AssetsInstructionOriginal target Sustainable Cities Final instruction annotation: multi-image pairs Editing Editing CPT poolFiltered slides + edit instructions Semantic consistency + presentation quality Nano-Banana-2 ++ ✓ Selected edit triplets Theme switching Image replacement Editing SFT records Figure 2: Slide data engine. (A) Continual pretraining: real decks are filtered, parsed into text and localized visual assets, and annotated with short requests, detailed descriptions, and style_json; the same target slide yields text-to-slide, template-conditioned, and multi-image records, and ex- ecuted PPTX edits yield source–instruction–target triplets. (B) Supervised fine-tuning: Nano- Banana-2 synthesizes targets from LLM concepts, SlideShare content, and paper-derived slide spec- ifications; multi-image records reuse assets extracted from synthesized targets; editing records com- bine selected CPT triplets with model-generated theme switching and image replacement. 3.1TASK FORMULATION We formulate slide creation as conditional visual synthesis y = G θ (x,I) over a textual condition x and optional input images I . In text-to-slide generation, I =∅; in template-conditioned generation, I is a reference slide that specifies the visual style; in multi-image grounded generation, I holds one or more visual assets that must be preserved in the composition; in slide editing, I is the source slide and x the requested change, with unrelated content kept stable. Generation instructions take three forms: a short caption, a detailed description of content and visual design, and a structured style_json of the page composition; editing uses a natural-language instruction. The same interfaces serve data construction and evaluation, so the training tasks align with VisSlideBench. 3.2CONTINUAL PRETRAINING DATA Real-slide sources and parsing. We collect real presentations from two complementary sources: author-released conference talks with dense text, equations, and scientific figures, and a broader collection spanning 39 topical categories with more diverse templates and styles. We use Qwen3.5- 27B (Qwen Team, 2026a) to remove low-quality and non-slide content, then parse the retained pages with PaddleOCR-VL (Cui et al., 2025) into text in reading order and localized visual elements, which form the shared content representation for the generation tasks. Generation task construction. From each parsed slide, we construct three generation views that share the original rendered slide as the target while using different conditioning inputs. An open 4 Preprint VLM generates a short request, a detailed description, and a structured style_json specifica- tion from the slide, its OCR transcription, and extracted visual elements. Text-to-slide records use the textual condition alone; the instruction therefore describes the whole slide, visual assets in- cluded. Template-conditioned records pair the target with an adjacent slide from the same deck as the reference; we retain pairs with consistent backgrounds, colors, typography, and decorative styles. Multi-image grounded records reuse one to four localized visual elements as conditioning inputs and describe how they should be incorporated into the slide. Executable editing pairs. For editing CPT, we construct source–instruction–target triplets through executable changes to PPTX files drawn from real editable presentations and synthetic PPTX files built from OCR text and presentation backgrounds. Seven operations cover text modification, block addition and removal, background updates, and changes to color, font, and typography; each is executed in the PPTX file and rendered, and an LLM describes the realized change from the source image, target image, and executed action. We remove source slides with layout or rendering defects and pairs with failed edits or inconsistent instructions. Across the four task branches, continual pretraining contains over 13M records. 3.3SUPERVISED FINE-TUNING DATA Generation SFT. Generation SFT content comes from three sources: LLM-generated concepts across diverse domains, structured content from real SlideShare decks, and research papers con- verted into slide-level specifications. Nano-Banana-2 renders the content into target slides under diverse visual styles; for template-conditioned generation, we first obtain a reference slide from a curated template library or by asking Nano-Banana-2 to create one, then generate the target from the reference and new content. For multi-image grounded generation, we extract charts, tables, and other visual regions from the synthesized targets as conditioning inputs. Qwen3.5-27B annotates the records with short requests, detailed descriptions, and structured style_json specifications, or with instructions describing how the assets should be incorporated, and we filter invalid inputs and inconsistent instructions. Editing SFT. For the seven executable operations, we select high-quality triplets from the editing CPT pool by semantic consistency and presentation quality, and add theme switching and image replacement examples generated by Nano-Banana-2 from filtered real slides, recaptioned so that each instruction describes the realized change. Across the four task branches, the SFT resource contains about 1.9M records (Appendix A.4). 4VISSLIDEBENCH 4.1BENCHMARK TASK SUITE VisSlideBench (Figure 5 in Appendix B) evaluates the generation and editing of individual slide images across the four tasks defined in Section 3.1. It contains 1,527 manually screened instances: 943 in VisSlideBench-Gen, covering text-to-slide, template-conditioned, and multi-image grounded generation, and 584 in VisSlideBench-Edit. Generation prompts range from short requests to de- tailed descriptions and structured style_json specifications, covering instructions from users and agents. Among the benchmarks compared in Appendix B.1, VisSlideBench is the only one that jointly covers all four creation interfaces, three prompt granularities, and task-specific measures of content fidelity, instruction compliance, aesthetics, and visual grounding. 4.2BENCHMARK CONSTRUCTION Generation. VisSlideBench-Gen combines 493 cases derived from real presentations with 450 cases whose target slides are generated by Nano-Banana-2 (Google, 2026). The real cases preserve authored content and design patterns, while the synthetic cases broaden visual-design coverage. Following Section 3, we construct the instructions and visual inputs required for each case: no image for text-to-slide, one reference slide for template-conditioned generation, and one to four 5 Preprint assets for multi-image grounded generation. We manually screen target slides and instructions, removing slides of poor visual quality and prompts that are ambiguous or inconsistent. Editing. VisSlideBench-Edit uses a subset of the editing data reserved for evaluation (Section 3.2); because these cases come from the same pipeline as the editing training data, the split measures in- distribution editing quality (Appendix E). Each instance contains a source slide, an edit instruction, and the corresponding target. We remove ambiguous instructions, invalid pairs, imperceptible edits, and severe visual artifacts through manual screening. The nine operations cover text modification, block addition and removal, background updates, changes to color, font, and typography, image replacement, and theme switching. Appendix B.2 reports task counts, source composition, domains, and editing operations, and describes how all cases are held out from training. 4.3EVALUATION PROTOCOL We use OCR to evaluate text rendering and GPT-4.1 (OpenAI, 2025) to judge visual content, com- pliance with design instructions, and presentation quality. The criteria are evaluated separately be- fore task-level aggregation; the text-scoring formula and additional evaluation details appear in Ap- pendix B.3. Content Faithfulness (CF). We score text rendering with the normalized edit distance of Gong et al. (2025), adapted to the text the instruction requires (Appendix B.3). For multi-image grounded generation, CF additionally checks whether every required input image is faithfully preserved and appropriately incorporated into the generated slide. Instruction Following (IF) and Aesthetic Quality (AQ). IF measures whether the output realizes the explicit design requirements in the instruction, judged along Background Compliance, Color Palette Adherence, Typography Compliance, Layout and Position Accuracy, Mood and Atmosphere Match, and Block-Level Style Fidelity. AQ measures the intrinsic visual quality of the slide and is judged from the output image alone, without the instruction, along Clarity, Layout, Style Coherence, and Presentation Professionalism. The separation matters because a slide can satisfy every design constraint yet remain visually weak, or look attractive while violating the instruction. Appendix B.3 defines each dimension. Editing. Adapting the SC/PQ framework of VIEScore (Ku et al., 2024) to slide editing, the eval- uator receives the source slide, editing instruction, and model output to score Instruction Following (S IF ), whether the requested change is applied, and Edit Consistency (S EC ), preservation of unre- lated text, images, layout, and styles. We define Semantic Consistency as S SC = min(S IF ,S EC ), so a high score requires both. Presentation Quality (S PQ ) uses the same four dimensions as gen- eration AQ and is likewise judged from the output image alone. The overall editing score is S Edit = p S SC S PQ . 4.4HUMAN VALIDATION We assess agreement with human judgments using the annotation protocol of EditScore (Luo et al., 2026): two groups of two domain experts each produce a consensus ranking of five anonymized model outputs per example, separately for each evaluation dimension, across all four tasks, yielding 2,153 valid pairwise comparisons. GPT-4.1 reaches a mean Kendall’s τ b of 0.640, a mean Spear- man’s ρ of 0.700, and pairwise agreement of 88.71%. Appendix B.4 gives the protocol and per-task results. 5INFOVIS TRAINING Slide-native model training. We adapt the pretrained image generator FLUX.2-klein-base- 9B (Black Forest Labs, 2026) to slide images, keeping its architecture and raising the maximum prompt length to 4,096 tokens. Training proceeds in three stages. Stage 1: Slide-native continual pretraining. We first continue pretraining the base model on large-scale text-to-slide data with short 6 Preprint Table 1: Main results on VisSlideBench. T2I, Template, and Multi-image report Content Faithful- ness (CF), Instruction Following (IF), and Aesthetic Quality (AQ); Edit reports Semantic Consis- tency (SC) and Presentation Quality (PQ). Overall averages the eleven metrics; bold marks the best in each group. TypeModel T2ITemplateMulti-imageEdit Overall CFIFAQCFIFAQCFIFAQSCPQ Proprietary GPT-Image-28.528.577.718.198.517.628.938.447.619.808.048.36 Nano-Banana-28.388.177.337.597.957.088.607.937.149.708.037.99 Qwen-Image-27.186.896.388.236.295.746.976.255.554.966.476.45 Open-source Qwen-Image6.506.345.17– Z-Image-Turbo6.666.845.79– LongCat-Image4.695.474.25– Qwen-Image-Edit–3.105.985.414.344.744.127.777.37– LongCat-Image-Edit–4.865.995.045.255.104.516.646.62– FLUX.2-dev6.607.085.296.287.385.226.547.135.206.127.186.37 FLUX.2-klein-base-9B6.297.115.196.447.245.406.576.995.325.986.726.30 HiDream-O1-Image6.825.624.685.975.144.505.604.904.172.233.774.85 BAGEL3.505.683.793.315.173.733.744.763.555.106.174.41 OmniGen23.895.133.143.755.823.344.064.903.232.825.224.12 InfoVis (ours)8.428.017.028.148.097.008.718.017.069.137.757.94 captions, long descriptions, and structured design prompts, adapting it to the slide domain and learn- ing shared priors for jointly coordinating text rendering, layout, visual hierarchy, and design. Stage 2: Four-task mixed continual pretraining. We then train on a mixture of the four task datasets, ex- tending these priors to style-reference following, multi-image grounding, and image editing. Stage 3: Task-specific supervised fine-tuning and model fusion. Finally, we perform one epoch of text- to-slide SFT to reduce the generation–editing distribution gap, fine-tune four task-specific models from the resulting shared checkpoint θ shared , and linearly fuse them into the final model, following weight-space merging of fine-tuned checkpoints (Wortsman et al., 2022; Ilharco et al., 2023). Document-to-slide workflow. We use InfoVis in a document-to-slide workflow to produce com- plete presentations: a parser extracts text and visual assets, content and design planners convert them into slide plans and structured style_json prompts, InfoVis renders candidate slides, and an external verifier selects the outputs, with a selected slide serving as the template reference for subsequent pages. Appendix D details the planning, generation, and PowerPoint export steps. 6EXPERIMENTS 6.1EXPERIMENTAL SETUP Training follows Section 5 and Appendix C.1. We compare with the proprietary GPT-Image-2 (Ope- nAI, 2026), Nano-Banana-2 (Google, 2026), and Qwen-Image-2 (Qwen Team, 2026b) and with open-source models, each evaluated on the tasks its official interface supports, with recommended inference settings and the same VisSlideBench protocol; prompts longer than a model’s text window are truncated (Appendix C.5). 6.2MAIN RESULTS ON VISSLIDEBENCH Table 1 reports the main results on VisSlideBench; overall scores are reported only for models that support all four tasks. Tables 12–15 in Appendix C.2 break each task down by evaluation dimension, and Appendix F presents qualitative results for all four tasks and the document-to-slide workflow. Slide-specific post-training drives the gains. InfoVis achieves an overall score of 7.94, rank- ing highest among open-source models and remaining competitive with Nano-Banana-2. A paired bootstrap over benchmark cases gives intervals for the gaps to Nano-Banana-2 and GPT-Image-2 (Appendix C.1). Starting from FLUX.2-klein-base-9B, slide-specific training improves the overall score by 1.64 points. The gains are consistent across all three generation tasks. Content Faithfulness 7 Preprint Table 2: BizGenEval Slides (Li et al., 2026), hard/easy scores from the official leaderboard; Nano-Banana-Pro and Seedream-5.0, omitted here, also outperform InfoVis. ModelHard / Easy GPT-Image-2.084.0 / 95.0 Nano-Banana-2.073.8 / 95.8 InfoVis (ours)46.0 / 79.8 GPT-Image-1.540.8 / 89.2 Seedream-4.533.8 / 71.0 Wan2.6-T2I27.1 / 56.5 HunyuanImage-3.019.3 / 47.0 Seedream-4.018.5 / 67.8 FLUX.2-dev5.5 / 43.2 Table 3:SlidesGen-Bench Aesthetics (Yang et al., 2026). U, E, H, R: Usability, Engagement, Harmony, Rhythm; Aesthetics is their sum. Bold marks our system, which uses GPT-4.1 for plan- ning and the Qwen3.5-27B verifier with K = 2 (Appendix D). SystemUEHRAesthetics Skywork-Banana5.628.30-0.4713.8427.28 InfoVis + workflow5.658.14-0.5613.8627.09 Kimi-Banana5.726.41-0.5515.0126.58 NotebookLM4.137.32-0.3511.7222.82 Zhipu4.877.52-1.6011.2722.06 Skywork4.837.60-1.189.4420.69 Kimi-Standard4.616.28-1.7510.1219.25 Kimi-Smart4.137.99-1.888.0618.30 Gamma5.316.31-1.516.9917.09 Quark5.037.41-1.916.3316.86 improves the most (+1.70 to +2.14), followed by Aesthetic Quality (+1.60 to +1.83) and Instruc- tion Following (+0.85 to +1.02). These results show that slide training mainly improves faithful rendering of dense text and supplied assets, while the base model already follows style instructions reasonably well. Editing Semantic Consistency also rises by 3.15 points, indicating stronger joint performance on edit execution and preservation. Unified training preserves specialist performance. Existing unified open-source models per- form substantially worse on slides: BAGEL, OmniGen2, and HiDream-O1-Image score 4.1–4.9 overall and trail the editing specialist Qwen-Image-Edit on editing. InfoVis uses one model for all four tasks and leads every open-source baseline, specialist or unified, on all eleven metrics. Com- pared with our task-specific checkpoints (Table 4), the fused model loses only 0.06 on editing SC (9.13 versus 9.19) and improves the remaining metrics. The earlier shared checkpoint retains a slightly higher Template CF (8.25 versus 8.14). The training-stage ablation in Section 6.4 further shows that text-to-slide continual pretraining alone degrades editing, while joint supervision restores and retains all four capabilities. The remaining gap is concentrated in aesthetics. Against GPT-Image-2, InfoVis is closest on Content Faithfulness across all three generation tasks, with gaps of 0.05–0.22. The gaps increase to 0.42–0.56 on Instruction Following and 0.55–0.69 on Aesthetic Quality. The same pattern appears against Nano-Banana-2: InfoVis matches or exceeds it on Content Faithfulness across all three tasks, achieves comparable Instruction Following, and trails on Aesthetic Quality. Editing shows a related pattern. InfoVis performs strongly on localized operations such as text, color, and typography edits, while theme switching and other global transformations remain more difficult (Table 15). The remaining gap therefore lies in visual organization and presentation polish. 6.3EXTERNAL BENCHMARK EVALUATION We further evaluate out-of-distribution (OOD) generalization on two external benchmarks. On Biz- GenEval (Li et al., 2026) (Table 2), which evaluates single-slide generation in the Slides domain, InfoVis reaches 46.0/79.8 on the hard/easy splits, the best result among all open-source models in this comparison and ahead of several proprietary systems. The remaining gap on the hard split reflects its knowledge- and reasoning-intensive cases, which slide-specific training does not target. On the Slides Aesthetics evaluation of SlidesGen-Bench (Yang et al., 2026) (Table 3), the complete document-to-slide workflow described in Section 5 achieves an Aesthetics score of 27.09, closely approaching the best reported result of 27.28. This shows that InfoVis transfers from single-slide generation to end-to-end presentation creation with visual quality on par with the best reported systems. We report the Aesthetics track only; the content and editability tracks were not run. 8 Preprint Table 4: Training-stage ablation; metric groups and Overall follow Table 1. Bold marks the best in each column. StageModel T2ITemplateMulti-imageEdit Overall CFIFAQCFIFAQCFIFAQSCPQ BaseFLUX.2-klein-base-9B6.297.115.196.447.245.406.576.995.325.986.726.30 CPT Stage-1 CPT: T2I only8.007.326.055.937.034.576.166.234.891.604.785.69 Stage-2 CPT: four-task mixture8.157.426.228.047.776.338.387.546.297.877.137.38 Task SFT Shared SFT8.287.806.838.257.876.818.507.826.828.447.607.73 Shared SFT + T2I SFT8.287.726.848.127.816.728.247.906.947.927.437.63 Shared SFT + Template SFT8.197.866.968.057.946.847.507.866.806.497.177.42 Shared SFT + Multi-image SFT8.267.736.867.657.666.138.127.416.286.867.367.30 Shared SFT + Edit SFT8.247.786.587.907.486.318.207.816.599.197.687.61 FinalInfoVis8.428.017.028.148.097.008.718.017.069.137.757.94 1248 Candidates 7.25 7.50 7.75 8.00 8.25 8.50 8.75 9.00 Score IF BG Color Typo Layout Mood Block 1248 Candidates 6.6 6.8 7.0 7.2 7.4 7.6 7.8 AQ Clarity LayoutQ StyleQ Prof 1248 Candidates 7.0 7.3 7.6 7.9 8.2 8.5 8.8 Aggregate Overall CF IF AQ Aggregate IF/AQ = mean of submetrics; higher is better 0246810 Mean of CF, IF and AQ (higher is better) InfoVis (ours) FLUX.2-dev FLUX.2-klein-base-9B Z-Image-Turbo Qwen-Image BAGEL GPT-Image-2 Qwen-Image-2 Nano-Banana-2 Prompt categories ShortLongStyleJSON (a) Verifier-time scaling(b) Prompt categories Figure 3: Test-time scaling and prompt categories on the T2I split. (a) Best-of-K selection for K ∈1, 2, 4, 8: IF dimensions, AQ dimensions, and aggregates. (b) Mean of per-slide CF, IF, and AQ over all 317 cases by prompt category. Vertical ranges differ across panels. 6.4ABLATION ON TRAINING STAGES Table 4 shows that slide adaptation does not transfer uniformly across capabilities. Stage 1 T2I-only CPT raises T2I Content Faithfulness from 6.29 to 8.00 but collapses editing Semantic Consistency from 5.98 to 1.60; four-task mixed CPT restores all four groups and lifts the overall score to 7.38. Shared SFT on text-to-slide data alone raises every group, including editing SC to 8.44. Each task- specific SFT then lowers the overall score below the shared checkpoint (7.73). Part of the loss is cross-task interference: template SFT drops editing SC to 6.49. Part of it is a shift within the task: the generation SFT pools are synthetic, and multi-image SFT lowers its own task from 8.50 to 8.12 CF, mostly on cases built from real decks (Appendix C.4). Weighted averaging keeps nearly the peak of each specialization (editing SC 9.13 against 9.19) while lifting the other columns, giving the strongest unified model (7.94). 6.5MORE ANALYSIS Test-time scaling. Using Qwen3.5-27B (Qwen Team, 2026a) as a slide-quality verifier (Ap- pendix C.1), best-of-8 selection on a random subset of 50 T2I cases raises the overall score from 7.96 to 8.11 (Figure 3a). The largest gain is in Text Rendering (8.57 to 8.80), whereas IF and AQ improve more modestly and not monotonically across dimensions, so verifier-guided sampling helps most on criteria the verifier resolves reliably. This matches the ordering of the remaining gap in Section 6.2. Prompt categories. Figure 3b splits the text-to-slide benchmark into 115 short requests, 109 de- tailed descriptions, and 93 StyleJSON specifications. InfoVis scores 8.00, 8.08, and 7.28 and trails GPT-Image-2 by 0.40 to 0.50 on every category. StyleJSON separates the models: from detailed descriptions to StyleJSON, GPT-Image-2, Nano-Banana-2, and InfoVis each lose under 0.9 points, whereas the open-source baselines lose 1.5 to 4.2, FLUX.2-dev and BAGEL included, which re- ceive every prompt in full. Three open-source baselines ran with text windows that clip StyleJSON 9 Preprint prompts; Appendix C.5 rescores them on the prompts they received in full, and InfoVis stays ahead of all of them. 7CONCLUSION We present a unified framework for slide image generation and editing. We formulate four comple- mentary tasks, construct a large-scale slide-native training resource and introduce VisSlideBench for multidimensional evaluation. Trained on these resources, our model InfoVis achieves the strongest overall open-source performance on VisSlideBench, within 0.05 of Nano-Banana-2 and 0.42 of GPT-Image-2, and supports an image-centric document-to-slide workflow whose aesthetic quality is on par with the best reported systems. AI USE STATEMENT We used AI assistants in three ways. First, for writing and analysis: to draft and revise passages of the text under the authors’ direction, to check grammar and spelling, and to run scripts that aggregate stored evaluation outputs into the tables and statistics reported in the appendix; all research ideas, claims, numbers, and final content were reviewed and confirmed by the authors. Second, for literature search: to help locate related work and background material; every cited reference was verified by the authors. Third, as part of the method: language and multimodal models annotate and filter training data, synthesize SFT targets and benchmark cases, and serve as the automatic evaluator, as described in Sections 3 and 4. The synthetic data do not represent real individuals or observations, and the evaluator’s agreement with human judgments is reported in Section 4.4. REPRODUCIBILITY STATEMENT Section 5 and Appendix C.1 give the training recipe and hyperparameters; Appendix A describes the data sources, annotation and filtering procedures, and record counts; and Appendix B de- tails the benchmark composition, evaluation metrics, and human validation protocol. We will re- lease the InfoVis weights, the training and evaluation code, the training data and data engine, and VisSlideBench. ETHICS STATEMENT Data sources and release. The real-slide corpus consists of talk decks that their authors released publicly and presentations collected from presentation-sharing and PPT aggregation websites (Ap- pendix A.1); the synthetic targets are rendered by Nano-Banana-2 and annotated with Qwen3.5-27B and GPT-4.1. We will release the model weights, code, data engine, training data, and VisSlideBench for research use, with the source of every real-slide record recorded so that individual items can be traced and removed on request; the synthetic data do not depict real individuals. Human valida- tion. The human rankings in Section 4.4 were produced by four domain experts in slide design and visual generation who ranked anonymized model outputs; the study collected no personal data from the annotators. Intended use. InfoVis generates and edits slide images from user instructions and supplied assets. Like other image generators it can render text that is inaccurate or misleading, and the document-to-slide workflow should be used with the source document at hand; we do not target generation of content about real persons. REFERENCES Black Forest Labs. FLUX.2: Frontier Visual Intelligence. https://bfl.ai/blog/flux-2, 2025. Black Forest Labs. FLUX.2 [klein]: Towards interactive visual intelligence. https://bfl.ai/ blog/flux2-klein-towards-interactive-visual-intelligence, 2026. Qi Cai, Jingwen Chen, Chengmin Gao, Zijian Gong, Yehao Li, Yingwei Pan, Yi Peng, Zhaofan Qiu, Kai Yu, Yiheng Zhang, et al. Hidream-o1-image: A natively unified image generative foundation model with pixel-level unified transformer. arXiv preprint arXiv:2605.11061, 2026. 10 Preprint Jingye Chen, Yupan Huang, Tengchao Lv, Lei Cui, Qifeng Chen, and Furu Wei.Textd- iffuser:Diffusion models as text painters.In Alice Oh, Tristan Naumann, Amir Globerson, Kate Saenko, Moritz Hardt, and Sergey Levine (eds.), Advances in Neu- ral Information Processing Systems 36: Annual Conference on Neural Information Pro- cessing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023, 2023.URL http://papers.nips.c/paper_files/paper/2023/hash/ 1df4afb0b4ebf492a41218ce16b6d8df-Abstract-Conference.html. Jingye Chen, Yupan Huang, Tengchao Lv, Lei Cui, Qifeng Chen, and Furu Wei. Textdiffuser- 2: Unleashing the power of language models for text rendering.In Ales Leonardis, Elisa Ricci, Stefan Roth, Olga Russakovsky, Torsten Sattler, and Gül Varol (eds.), Computer Vision - ECCV 2024 - 18th European Conference, Milan, Italy, September 29-October 4, 2024, Proceed- ings, Part V, Lecture Notes in Computer Science, p. 386–402. Springer, 2024. doi: 10.1007/ 978-3-031-72652-1\_23. URL https://doi.org/10.1007/978-3-031-72652-1_ 23. Sixiang Chen, Jianyu Lai, Jialin Gao, Tian Ye, Haoyu Chen, Hengyu Shi, Shitong Shao, Yun- long Lin, Song Fei, Zhaohu Xing, Yeying Jin, Junfeng Luo, Xiaoming Wei, and Lei Zhu. Postercraft: Rethinking high-quality aesthetic poster generation in a unified framework. CoRR, abs/2506.10741, 2025. doi: 10.48550/ARXIV.2506.10741. URL https://doi.org/10. 48550/arXiv.2506.10741. Xin-Sheng Chen, Jiayu Zhu, Pei-lin Li, Hanzheng Wang, Shuojin Yang, and Meng-Hao Guo. Presentbench: A fine-grained rubric-based benchmark for slide generation. arXiv preprint arXiv:2603.07244, 2026. Benlei Cui, Fangao Zeng, Weitao Jiang, Yuwen Zhai, Haiwen Hong, Longtao Huang, Hui Xue, Wenxiang Shang, and Pipei Huang. simpleposter: a simple baseline for product poster generation. arXiv preprint arXiv:2605.08784, 2026. Cheng Cui, Ting Sun, Suyin Liang, Tingquan Gao, Zelun Zhang, Jiaxuan Liu, Xueqing Wang, Changda Zhou, Hongen Liu, Manhui Lin, Yue Zhang, Yubo Zhang, Handong Zheng, Jing Zhang, Jun Zhang, Yi Liu, Dianhai Yu, and Yanjun Ma. Paddleocr-vl: Boosting multilingual document parsing via a 0.9b ultra-compact vision-language model, 2025. URL https://arxiv.org/ abs/2510.14528. Chaorui Deng, Deyao Zhu, Kunchang Li, Chenhui Gou, Feng Li, Zeyu Wang, Shu Zhong, Wei- hao Yu, Xiaonan Nie, Ziang Song, Guang Shi, and Haoqi Fan. Emerging properties in unified multimodal pretraining. arXiv preprint arXiv:2505.14683, 2025. Yifan Gao, Zihang Lin, Chuanbin Liu, Min Zhou, Tiezheng Ge, Bo Zheng, and Hong- tao Xie.Postermaker:Towards high-quality product poster generation with ac- curate text rendering.In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2025, Nashville, TN, USA, June 11-15, 2025, p. 8083–8093. Computer Vision Foundation / IEEE, 2025.doi:10.1109/CVPR52734.2025.00757. URL https://openaccess.thecvf.com/content/CVPR2025/html/Gao_ PosterMaker_Towards_High-Quality_Product_Poster_Generation_with_ Accurate_Text_Rendering_CVPR_2025_paper.html. Jiaxin Ge, Zora Zhiruo Wang, Xuhui Zhou, Yi-Hao Peng, Sanjay Subramanian, Qinyue Tan, Maarten Sap, Alane Suhr, Daniel Fried, Graham Neubig, et al. Autopresent: Designing structured visuals from scratch. In Proceedings of the Computer Vision and Pattern Recognition Conference, p. 2902–2911, 2025. Lixue Gong, Xiaoxia Hou, Fanshi Li, Liang Li, Xiaochen Lian, Fei Liu, Liyang Liu, Wei Liu, Wei Lu, Yichun Shi, Shiqi Sun, Yu Tian, Zhi Tian, Peng Wang, Xun Wang, Ye Wang, Guofeng Wu, Jie Wu, Xin Xia, Xuefeng Xiao, Linjie Yang, Zhonghua Zhai, Xinyu Zhang, Qi Zhang, Yuwei Zhang, Shijia Zhao, Jianchao Yang, and Weilin Huang. Seedream 2.0: A native chinese-english bilingual image generation foundation model, 2025. URL https://arxiv.org/abs/2503.07703. Google. Nano banana 2: Combining pro capabilities with lightning-fast speed. https://blog. google/innovation-and-ai/technology/ai/nano-banana-2/, 2026. 11 Preprint Xiwei Hu, Haokun Chen, Zhongqi Qi, Hui Zhang, Dexiang Hong, Jie Shao, and Xinglong Wu. Dreamposter: A unified framework for image-conditioned generative poster design. CoRR, abs/2507.04218, 2025. doi: 10.48550/ARXIV.2507.04218. URL https://doi.org/10. 48550/arXiv.2507.04218. Zheng Huang, Xukai Liu, Tianyu Hu, Kai Zhang, and Ye Liu. Pptbench: Towards holistic evalu- ation of large language models for powerpoint layout and design understanding. arXiv preprint arXiv:2512.02624, 2025. Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Suchin Gururangan, Ludwig Schmidt, Hannaneh Hajishirzi, and Ali Farhadi. Editing models with task arithmetic. In International Conference on Learning Representations, 2023. Daesik Jang, Morgan Lindsay Heisler, Linzi Xing, Yifei Li, Edward Wang, Ying Xiong, Yong Zhang, and Zhenan Fan. Deckbench: Benchmarking multi-agent frameworks for academic slide generation and editing. In Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 2, p. 9159–9170, 2026. Max Ku, Dongfu Jiang, Cong Wei, Xiang Yue, and Wenhu Chen. Viescore: Towards explain- able metrics for conditional image synthesis evaluation. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar (eds.), Proceedings of the 62nd Annual Meeting of the Association for Computa- tional Linguistics (Volume 1: Long Papers), ACL 2024, Bangkok, Thailand, August 11-16, 2024, p. 12268–12290. Association for Computational Linguistics, 2024. doi: 10.18653/V1/2024. ACL-LONG.663. URL https://doi.org/10.18653/v1/2024.acl-long.663. Yan Li, Zezi Zeng, Ziwei Zhou, Xin Gao, Muzhao Tian, Yifan Yang, Mingxi Cheng, Qi Dai, Yuqing Yang, Lili Qiu, et al. Bizgeneval: A systematic benchmark for commercial visual content gener- ation. arXiv preprint arXiv:2603.25732, 2026. Chengzhi Liu, Yuzhe Yang, Kaiwen Zhou, Zhen Zhang, Yue Fan, Yanan Xie, Peng Qi, and Xin Eric Wang. Presenting a paper is an art: Self-improvement aesthetic agents for academic presentations. arXiv preprint arXiv:2510.05571, 2025. Xin Luo, Jiahao Wang, Chenyuan Wu, Shitao Xiao, Xiyan Jiang, Defu Lian, Jiajun Zhang, Dong Liu, and Zheng Liu. Editscore: Unlocking online rl for image editing via high-fidelity reward modeling. In International Conference on Learning Representations, volume 2026, p. 33027– 33056, 2026. Jian Ma, Mingjun Zhao, Chen Chen, Ruichen Wang, Di Niu, Haonan Lu, and Xiaodong Lin. Glyphdraw: Learning to draw chinese characters in image synthesis models coherently. CoRR, abs/2303.17870, 2023. doi: 10.48550/ARXIV.2303.17870. URL https://doi.org/10. 48550/arXiv.2303.17870. Dongxing Mao, Alex Jinpeng Wang, Jiahao Tang, Kevin Qinghong Lin, Linjie Li, Zhengyuan Yang, Lijuan Wang, Min Li, and Jingru Tan. Residual decoder adapter: Id-preserving tokenizer adaption for autoregressive text rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, p. 22017–22027, 2026. Meituan LongCat Team, Hanghang Ma, Haoxian Tan, Jiale Huang, Junqiang Wu, Jun-Yan He, Lishuai Gao, Songlin Xiao, Xiaoming Wei, Xiaoqi Ma, Xunliang Cai, Yayong Guan, and Jie Hu. Longcat-image technical report. arXiv preprint arXiv:2512.07584, 2025. OpenAI. Introducing gpt-4.1 in the api. https://openai.com/index/gpt-4-1/, 2025. Accessed: 2026-07-23. OpenAI.Introducingchatgptimages2.0. https://openai.com/index/ introducing-chatgpt-images-2-0/, 2026. Yuyang Peng, Shishi Xiao, Keming Wu, Qisheng Liao, Bohan Chen, Kevin Lin, Danqing Huang, Ji Li, and Yuhui Yuan.Bizgen: Advancing article-level visual text rendering for infographics generation.In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2025, Nashville, TN, USA, June 11-15, 2025, p. 23615–23624. Com- puter Vision Foundation / IEEE, 2025.doi:10.1109/CVPR52734.2025.02199.URL 12 Preprint https://openaccess.thecvf.com/content/CVPR2025/html/Peng_BizGen_ Advancing_Article-level_Visual_Text_Rendering_for_Infographics_ Generation_CVPR_2025_paper.html. Qwen Team. Qwen3.5: Towards native multimodal agents, February 2026a. URL https:// qwen.ai/blog?id=qwen3.5. Qwen Team. Qwen-image-2.0: Professional infographics, exquisite photorealism. https:// qwen.ai/blog?id=qwen-image-2.0, 2026b. Xincheng Shuai, Ziye Li, Henghui Ding, and Dacheng Tao. Glyphprinter: Region-grouped direct preference optimization for glyph-accurate visual text rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, p. 7674–7683, 2026. Yuxiang Tuo, Yifeng Geng, and Liefeng Bo. Anytext2: Visual text generation and editing with customizable attributes. CoRR, abs/2411.15245, 2024a. doi: 10.48550/ARXIV.2411.15245. URL https://doi.org/10.48550/arXiv.2411.15245. Yuxiang Tuo, Wangmeng Xiang, Jun-Yan He, Yifeng Geng, and Xuansong Xie. Anytext: Multi- lingual visual text generation and editing. In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024. OpenReview.net, 2024b. URL https://openreview.net/forum?id=ezBH9WE9s2. Alex Jinpeng Wang, Linjie Li, Zhengyuan Yang, Lijuan Wang, and Min Li. Beyond words: Advanc- ing long-text image generation via multimodal autoregressive models. CoRR, abs/2503.20198, 2025a. doi: 10.48550/ARXIV.2503.20198. URL https://doi.org/10.48550/arXiv. 2503.20198. Alex Jinpeng Wang, Dongxing Mao, Jiawei Zhang, Weiming Han, Zhuobai Dong, Linjie Li, Yiqi Lin, Zhengyuan Yang, Libo Qin, Fuwei Zhang, Lijuan Wang, and Min Li. Textatlas5m: A large- scale dataset for dense text image generation. CoRR, abs/2502.07870, 2025b. doi: 10.48550/ ARXIV.2502.07870. URL https://doi.org/10.48550/arXiv.2502.07870. Zhendong Wang, Jianmin Bao, Shuyang Gu, Dong Chen, Wengang Zhou, and Houqiang Li.Designdiffusion:High-quality text-to-design image generation with diffusion models.In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2025, Nashville, TN, USA, June 11-15, 2025, p. 20906–20915. Computer Vision Foundation / IEEE, 2025c.doi:10.1109/CVPR52734.2025.01947.URL https://openaccess.thecvf.com/content/CVPR2025/html/Wang_ DesignDiffusion_High-Quality_Text-to-Design_Image_Generation_ with_Diffusion_Models_CVPR_2025_paper.html. Mitchell Wortsman, Gabriel Ilharco, Samir Yitzhak Gadre, Rebecca Roelofs, Raphael Gontijo- Lopes, Ari S Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, and Ludwig Schmidt. Model soups: averaging weights of multiple fine-tuned models improves ac- curacy without increasing inference time. In International Conference on Machine Learning, p. 23965–23998, 2022. Chenfei Wu, Jiahao Li, Jingren Zhou, Junyang Lin, Kaiyuan Gao, Kun Yan, Sheng ming Yin, Shuai Bai, Xiao Xu, Yilei Chen, Yuxiang Chen, Zecheng Tang, Zekai Zhang, Zhengyi Wang, An Yang, Bowen Yu, Chen Cheng, Dayiheng Liu, Deqing Li, Hang Zhang, Hao Meng, Hu Wei, Jingyuan Ni, Kai Chen, Kuan Cao, Liang Peng, Lin Qu, Minggang Wu, Peng Wang, Shuting Yu, Tingkun Wen, Wensen Feng, Xiaoxiao Xu, Yi Wang, Yichang Zhang, Yongqiang Zhu, Yujia Wu, Yuxuan Cai, and Zenan Liu. Qwen-image technical report, 2025. URL https://arxiv.org/abs/ 2508.02324. Chenyuan Wu, Jiahao Wang, Pengfei Zheng, Ruiran Yan, Shitao Xiao, Xin Luo, Yueze Wang, Wanli Li, Xiyan Jiang, Yexin Liu, et al. Omnigen2: Towards instruction-aligned multimodal generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, p. 21964–21975, 2026. 13 Preprint Yunqiao Yang, Wenbo Li, Houxing Ren, Zimu Lu, Ke Wang, Zhiyuan Huang, Zhuofan Zong, Mingjie Zhan, and Hongsheng Li. Slidesgen-bench: Evaluating slides generation via compu- tational and quantitative metrics. arXiv preprint arXiv:2601.09487, 2026. Z-Image Team. Z-image: An efficient image generation foundation model with single-stream diffu- sion transformer. arXiv preprint arXiv:2511.22699, 2025. Le Zhuo, Songhao Han, Yuandong Pu, Boxiang Qiu, Sayak Paul, Yue Liao, Yihao Liu, Jie Shao, Xi Chen, Si Liu, and Hongsheng Li. Factuality matters: When image generation and editing meet structured visuals. In International Conference on Learning Representations (ICLR), 2026. ASLIDE DATA ENGINE DETAILS A.1DATA SOURCES, FILTERING, AND PARSING Generation sources. We collect presentations from two complementary sources. Academic data comprise talk decks released by their authors from CVPR, ICCV, ECCV, ICLR, ICML, and NeurIPS across multiple years. These decks contain substantial text, mathematical notation, equations, and data figures. The other source comprises decks from presentation-sharing and PPT aggregation websites across 39 topical categories, including business, marketing, education, technology, health, and design. These presentations contribute additional templates, color schemes, and graphic styles. The collected corpus contains over 72K decks and 2.2M slide pages. Deck filtering. Crawled material includes scanned documents, web or application screenshots, and nearly empty pages. We sample several pages from each deck and use Qwen3.5-27B (Qwen Team, 2026a) to assess slide authenticity and visual quality. The rubric accommodates plain backgrounds and dense mathematical notation in academic slides. We retain decks whose average score exceeds a predefined threshold. Page parsing. We render each retained deck into page images at high resolution and parse every page with PaddleOCR-VL (Cui et al., 2025). Its Markdown/JSON transcription preserves reading order and recognizes text, mathematical formulas in LaTeX, and tables. The parser also localizes figures, charts, and tables. We crop these regions from the rendered page and store them as visual assets for annotation and conditioning. A.2GENERATION RECORD CONSTRUCTION Text-to-slide generation. Each record contains an instruction and a target slide image. We con- dition Qwen3.5-27B (Qwen Team, 2026a) on the slide image, OCR transcription, and localized visual assets. It produces a short caption, a detailed 300–800 word description, and a structured style_json specification describing page composition, content blocks, positions, and visual styles. The annotations describe visual assets in detail and preserve the recognized text. The gener- ator receives the instruction without the visual assets. Template-conditioned generation. We pair each target with an adjacent slide from the same deck. Adjacent pages often share visual style while differing in content and layout. We sample a template request from a predefined pool and prepend it to the corresponding T2I caption, instructing the generator to follow the reference style. A VLM checks consistency in background, color scheme, typography, and decorative style. It allows differences in content and layout; pairs below a prede- fined threshold are discarded. Multi-image grounded generation. Each record contains an instruction, localized visual assets, and the original slide as the target. The annotation model receives the target image, OCR transcrip- tion, and assets, and produces the same three instruction forms. Its prompt requires the supplied assets to appear in the output and describes their intended placement. We retain pages with one to four assets and remove inputs whose shorter side is below 64 pixels or whose aspect ratio exceeds 8:1. 14 Preprint A.3EDITING PAIR CONSTRUCTION Each editing record contains a source slide I src , an edit instruction x edit , and a target slide I tgt . Seven operations are constructed by editing PPTX files; theme switching and image replacement use an image editing model. The latter two operations are reserved for SFT. Source PPTX files. We use both synthetic and real presentations. For synthetic sources, we extract text blocks from the OCR annotations of the T2I data and combine them with backgrounds from presentation templates. An LLM generates Python-PPTX programs that arrange this content into editable pages. We execute the programs and render the pages as source images. For real sources, we split crawled PPTX files into individual pages and retain both the editable files and rendered images. We screen sources for text overlap, element overflow, truncation, layout quality, and aesthetics. Only successfully rendered pages that pass these checks enter edit construction. Executable operations. For background updates, color changes, font changes, typography changes, and block removal, we generate rule-based atomic actions over PPTX elements. For text modification, an LLM produces a structured action specifying the target element and its new text. A PPTX editing engine executes these actions, and we render the result to obtain the target image. An LLM observes the source image, target image, and executed action to describe the actual modifica- tion. We construct block addition by reversing filtered pairs for block removal and generating a new instruction for the reversed transition. Model-generated edits. For theme switching and image replacement, we first generate an edit instruction from a filtered screenshot of a real slide. Nano-Banana-2 (Google, 2026) then produces the target image, which we align to the source resolution. The replacement in the generated image can differ from the initial request. For image replacement, we therefore recaption the final source– target pair so that its instruction describes the visual change actually produced. Pair filtering. We check for missing or identical images, unsuccessful operations, and invalid in- structions. A VLM assesses whether the edit follows the instruction, consistency between source and target, and the visual quality of the edited slide. Checks for individual operations include verifying that the new text in a record for text modification appears explicitly in the instruction. A.4SUPERVISED FINE-TUNING RECORDS Target synthesis. We construct a shared pool of English 16:9 target slides from three content sources: concepts generated by an LLM across diverse domains, structured content derived from real SlideShare decks, and research papers converted into slide specifications. Nano-Banana-2 (Gemini 3.1 Flash Image Preview) (Google, 2026) renders these targets using styles defined in prompts or sampled from a curated presentation-template library. The resulting pool contains about 300K target slides. Generation annotations. Qwen3.5-27B (Qwen Team, 2026a) produces a short instruction, a de- tailed description preserving the rendered text, and a style_json specification of at most 4,096 tokens for each target. These views produce about 890K T2I records. The template branch contains about 300K records across two forms of style supervision: styles specified in text and styles pro- vided through a visual template. In the former, Nano-Banana-2 first renders a reference slide from the style text; in the latter, the library template is the reference. Both forms pair the target with this reference image. For multi-image grounding, PaddleOCR-VL extracts up to four charts, tables, or visual regions that do not overlap. Qwen3.5-27B generates instructions referring to these inputs. After filtering invalid crops and inconsistent instructions, this branch contains about 580K records. Editing selection. For the seven executable operations, Qwen3.5-27B scores candidate pairs from the editing pretraining pools for semantic consistency and presentation quality. We retain about 10K examples per operation. Theme switching and image replacement are excluded from CPT and used entirely for SFT, contributing about 40K and 30K records, respectively. Pairs for image replacement are additionally recaptioned with GPT-4.1 (OpenAI, 2025) to describe the edit realized in the target. The editing SFT pool contains about 140K records (Table 5). 15 Preprint Table 5: Editing operations and SFT records. The seven executable operations also contribute to CPT. Theme switching and image replacement are used only for SFT. OperationConstructionSFT records Text modificationPPTX execution≈10K Block additionReversed block removal≈10K Block removalPPTX execution≈10K Background updatePPTX execution≈10K Color changePPTX execution≈10K Font changePPTX execution≈10K Typography changePPTX execution≈10K Theme switchingNano-Banana-2≈40K Image replacementNano-Banana-2≈30K Total≈140K Table 6: Training records by branch. Counts are rounded. Multiple records can share a target slide or differ only in their instruction or visual condition. Training branchCPT recordsSFT records Text-to-slide≈5.16M≈890K Template-conditioned≈3.92M≈300K Multi-image grounded≈2.25M≈580K Slide editing≈2.12M≈140K Total> 13M≈1.9M A.5DATA STATISTICS Table 6 reports instruction records for the four training branches; several records can share a tar- get image. The 72K decks and 2.2M pages are the real sources before filtering, and the roughly 300K targets are the synthesized SFT pool. Figure 4 shows the distributions of domains and editing operations. BVISSLIDEBENCH DETAILS B.1COMPARISON WITH EXISTING BENCHMARKS Table 7 compares task coverage, evaluation criteria, and prompt formats. Reported sizes use dif- ferent units: BizGenEval contains 400 cases across five domains, including 80 slide cases, and PPTBench combines generation with detection, understanding, and modification. B.2BENCHMARK COMPOSITION Table 8 reports the generation cases by task and source. All three generation tasks include short requests, detailed descriptions, and structured style_json specifications. Generation examples cover academic, technology, business, science, education, media, lifestyle, and other presentation categories. Table 9 reports the nine editing operations. Each case contains a source slide, a natural-language editing instruction, and the corresponding target slide. The operations include changes to individual elements and broader changes to visual content or the slide theme. Figure 6 shows the distribution of cases over tasks, sources, prompt forms, domains, and editing operations. Separation from training data. The three generation splits share no target slide, verified by iden- tifier matching and manual screening. Every deck that contributes a VisSlideBench case is excluded from the training data, and no VisSlideBench target is used as a training target in any CPT or SFT record. The 584 editing cases, together with all training edit pairs that share a source or target image 16 Preprint Pretrain 5.16M SFT 0.89M Academic Real NeurIPS ICLR ICML CVPR Tech & IT Business & Finance Career & Professional Science & Eng. Marketing & Sales Health & Medicine Lifestyle & Leisure Arts & Media Public A f airs Education Sci. & Eng. Lifestyle Arts & Media General Pres. Public A f airs Education Business & Fin. Health Marketing Academic Career Tech & IT ECCV T2I 6.05M Instructions Academic Real Pretrain 2.25M SFT 0.58M NeurIPS ICLR ICML CVPR ECCV Tech & IT Business & Finance Science & Eng. Career & Professional Marketing & Sales Lifestyle & Leisure Arts & Media Health & Medicine Education Public A f airs Sci. & Eng. Lifestyle Arts & Media General Pres. Public A f airs Education Health Business & Fin. Academic Marketing Career Tech & IT IC 2.83M Instructions Academic Real Pretrain 3.92M SFT 0.30M NeurIPS ICLR ICML CVPR ECCV Tech & IT Business & Finance Career & Professional Science & Eng. Marketing & Sales Health & Medicine Lifestyle & Leisure Arts & Media Public A f airs Education Sci. & Eng. Public A f airs Business & Fin. Lifestyle Education Arts & Media Health General Pres. Marketing Ref 4.22M Instructions Pretrain 2.12M SFT 0.14M Add Block Background Update Change Color Change Font Change Typography Modify Text Content Remove Block Theme Switch Image Change Add Background Color Font Typography Text Remove Edit 2.26M Instructions Figure 4: Training record distributions by task and by CPT/SFT pool. Generation branches are further divided by source and domain, and the editing branch by operation. IC denotes multi-image grounded generation; Ref denotes template-conditioned generation. Counts are rounded. with them, are removed from the editing CPT and SFT pools. We verified these exclusions by iden- tifier matching over all training records. Table 16 reports results separately for real and synthetic cases. B.3EVALUATION DETAILS Content Faithfulness. Content Faithfulness scores the text the instruction requires the slide to ren- der. For every prompt form, GPT-4.1 reads the instruction alone and returns the strings it requires on the slide, such as titles, quoted copy, captions, and labels; a short request therefore yields fewer required strings t 1 ,...,t m than a detailed description or a style_json specification. PaddleOCR reads the generated slide, and the detected boxes are merged into text lines by position. Because a required string can span several lines, lines whose concatenation matches a required string after low- ercasing and whitespace removal (RapidFuzz ratio above 90) are joined; the joined and remaining lines form the candidate set G. Each required string is matched to its closest candidate under the Levenshtein distance over Unicode code points, with case, punctuation, and spaces counted as edits: N e = m X i=1 min g∈G Lev(t i ,g), N tgt = m X i=1 |t i |,(1) S text = max 10− 9 N e N tgt , 1 ,(2) which adapts the normalized edit-distance metric of Gong et al. (2025) to required-text matching and rescales it to [1, 10]. Text the model adds outside the matched candidates does not change the score; insertions inside a matched candidate count as edits. For multi-image grounded generation, 17 Preprint Table 7: Comparison of representative slide-creation benchmarks. A checkmark indicates explicit coverage in the task or evaluation protocol; a cross indicates that the feature is not covered. Size reports evaluation cases, with mixed tasks and slide subsets identified. Feature SlidesBench Ge et al. (2025) PPTBench Huang et al. (2025) BizGenEval Li et al. (2026) PresentBench Chen et al. (2026) SlidesGen- Bench Yang et al. (2026) VisSlideBench (ours) General Properties Size5854,439 mixed 400 total (80 slides) 2381891,527 Single-slide evaluation✓×✓ Image-centric systems included✓×✓ Task Coverage Text-to-single-slide✓×✓ Visual-reference template-to-slide ×✓ Multi-image-to-single-slide✓×✓ Slide editing×✓×✓ Evaluation Coverage Text-rendering fidelity✓×✓×✓ Instruction following✓ Visual aesthetics✓×✓ Source-document fidelity×✓× Input-image consistency✓×✓ Multi-dimensional evaluation✓×✓ Prompt Coverage Short prompt✓×✓ Long-form instruction×✓ Machine-structured design prompt ×✓×✓ Generation · Curation Real presentationsSynthetic slides T2ITemplateMulti-image Slide–instruction screening Manual screening ✓ Visual quality Check legibility and visible defects. ✓ Instruction clarity Check that the instruction is understandable. ✓ Slide–instruction consistency Check that the slide matches the instruction. Retained test cases Editing · Curation Held-out editing data Edit-pair screening Manual screening ✓ Valid pairs ✓ Unambiguous instructions ✓ Perceptible changes ✓ No severe artifacts Retained test cases Generation evaluation Multi-image case · input images Instruction: “Combine these figures on a slide titled Visualization Experiments, with concise captions...” Generated slide IF · Instruction Following · 9.17 Background compliance · 10/10 Clean white background; no texture or gradient. Color palette adherence · 10/10 Black text, white background and original figure colors match. Typography compliance · 9/10 Large black title and standard body type; no unwanted effects. Layout & position accuracy · 8/10 Correct figure arrangement. Mood & atmosphere match · 10/10 Clean, professional and technical; no decoration. Block-level style fidelity · 8/10 Labeled blocks and captions follow the request. IF CF · Content Faithfulness · 9.51 Text fidelity · 9.02/10 Apply OCR, then compute the minimum edit distance to the target text Image fidelity · 10/10 Evaluate whether every input image is faithfully preserved and incorporated into the slide. CF OCR Text matching against target text AQ · Aesthetic Quality · 6.50 Clarity · 7/10 Readable hierarchy, but dense captions and small axis labels. Layout · 6/10 Uneven spacing and a crowded bottom. Style coherence · 7/10 Figure styles and caption density differ. Presentation professionalism · 6/10 Usable academically; spacing and captions need polish. AQ Editing evaluation Instruction: Change the font of the body text “...Future Preparedness” to Georgia. Source imageEdited output SC · Semantic Consistency Instruction Following · 10/10 Body bullet text correctly changed to Georgia. Edit Consistency · 10/10 Title, subtitle, layout and decorations remain unchanged. SC SC = min(10, 10) = 10.00 PQ · Presentation Quality · 8.75 Clarity · 9/10 Clear title, subtitle and bullet hierarchy. Layout · 8/10 Dense left list leaves excessive empty space on the right. Style coherence · 9/10 Fonts, colors and sidebar elements remain consistent. Presentation professionalism · 9/10 Clean and professional; suitable for corporate use. PQ Edit = √(10.00 × 8.75) ≈ 9.35 MLLM as a judge (except OCR) Figure 5: VisSlideBench curation and evaluation. Left: task coverage and manual screening for generation and held-out editing data. Right: representative multi-image generation and editing cases illustrate the evaluation criteria with recorded scores and concise scoring explanations. CF describes the scoring procedure; editing PQ explanations summarize the four AQ dimensions. MLLM judges assess visual criteria, while OCR-based text matching evaluates text fidelity. GPT-4.1 (OpenAI, 2025) also judges whether every required input image is faithfully preserved and appropriately incorporated into the output; the multi-image CF score is the mean of S text and this image-preservation score. Instruction Following. Each dimension is judged against the corresponding requirement in the instruction. Background Compliance checks requested background colors, imagery, or visual treat- 18 Preprint Table 8: Composition of VisSlideBench-Gen. Counts refer to evaluation cases. The three prompt formats occur within each task. TaskVisual inputReal Synthetic Total Text-to-slideNone167150317 Template-conditionedOne reference slide147150297 Multi-image grounded One to four assets179150329 Total493450943 Table 9: Composition of VisSlideBench-Edit. OperationCases Text modification50 Block addition74 Block removal49 Background update52 Color change49 Font change49 Typography modification73 Image replacement103 Theme switching85 Total584 ment; Color Palette Adherence checks whether prescribed colors are used consistently and assigned to the intended elements; Typography Compliance checks requested font choices, weights, sizes, and emphasis; Layout and Position Accuracy evaluates the specified placement, alignment, and spatial relationships of slide elements; Mood and Atmosphere Match evaluates the requested overall visual tone; and Block-Level Style Fidelity checks local styling requirements for individual text, image, chart, and decorative blocks. Aesthetic Quality. The evaluator scores AQ from the output image alone and does not see the instruction. Clarity covers text legibility, contrast, and the strength of the visual hierarchy. Layout covers alignment, spacing, balance, and perceptual grouping. Style Coherence measures consis- tency across colors, typography, containers, and visual assets. Presentation Professionalism assesses whether the result appears polished, purposeful, and ready for an academic or business presentation. The four dimensions are averaged with equal weights into the AQ score. The editing scores are defined in Section 4.3. We use the resulting dimension scores independently when comparing the automatic evaluator with human rankings. The full generation and edit-evaluator prompts, including scoring criteria and output formats, are reproduced in Appendix B.5. B.4HUMAN VALIDATION PROTOCOL We follow the annotation protocol of EditScore (Luo et al., 2026) in two disjoint annotation batches covering all four VisSlideBench tasks. Each batch is ranked by a group of two annotators with expertise in slide design and visual generation. We adopt its two-annotator discussion protocol: the two annotators in a group discuss each example and submit one consensus ranking, so each batch yields a single ranking per example and dimension. For each example, human raters see five anonymized outputs from GPT-Image-2 (OpenAI, 2026), Qwen-Image-2 (Qwen Team, 2026b) (re- placed by Qwen-Image-Edit (Wu et al., 2025) for editing), InfoVis, FLUX.2-klein-base-9B (Black Forest Labs, 2026), and OmniGen2 (Wu et al., 2026) in randomized order. Raters rank Instruction Following and Aesthetic Quality for text-to-slide and template-conditioned generation, add Content Faithfulness for multi-image grounded generation, and rank Editing Success, Edit Consistency, and Presentation Quality for editing. Ties are allowed, and raters do not see model identities or automatic scores. 19 Preprint Real 167 Synthetic 150 Academic Tech & IT Business & Finance Marketing & Sales Career & Professional Science & Eng. Lifestyle & Leisure Public A f airs Arts & Media Health & Medicine Education Science & Eng. General Pres. Lifestyle & Leisure Public A f airs Arts & Media Education Health & Medicine Academic Tech & IT Career & Professional Marketing & Sales Business & Finance T2I 317 Instructions Real 179 Synthetic 150 Academic Tech & IT Business & Finance Career & Professional Marketing & Sales Lifestyle & Leisure Science & Eng. Public A f airs Arts & Media Education Health & Medicine IC Evaluation Science & Eng. Arts & Media Lifestyle & Leisure Health & Medicine Business & Finance Career & Professional Education Public A f airs General Pres. Marketing & Sales Academic Tech & IT IC 329 Instructions Real 147 Synthetic 150 Academic Business & Finance Arts & Media Career & Professional Lifestyle & Leisure Tech & IT Marketing & Sales Science & Eng. Public A f airs Education Health & Medicine Template Reference Science & Eng. Public A f airs Education General Pres. Lifestyle & Leisure Business & Finance Arts & Media Health & Medicine Marketing & Sales Ref 297 Instructions Image Change 103 Theme Switch 85 Add Block 74 Change Typography 73 Modify Text Content 50 Change Color 49 Change Font 49 Remove Block 49 Background Update 52 Edit 584 Instructions Figure 6: Distribution of VisSlideBench examples across tasks, sources, domains, and editing oper- ations. Table 10: Agreement between human rankings and GPT-4.1 scores. Rankings counts valid example– dimension pairs, and Pairs counts model pairs that are untied in both rankings. TaskExamples RankingsPairs τ b ρ Agree. (%) Text-to-slide3671548 0.729 0.79891.24 Template-conditioned3774502 0.613 0.67687.25 Multi-image grounded3288540 0.640 0.70290.00 Editing3295563 0.595 0.64586.32 Overall137328 2,153 0.640 0.70088.71 After filtering skipped or invalid records, 137 examples yield 328 valid dimension-level rankings. We assign average ranks to ties and compute Kendall’s τ b and Spearman’s ρ for each example and dimension, then report the mean across rankings (Table 10). Pairwise agreement pools model pairs for which neither the human ranking nor the automatic scores contain a tie. B.5BENCHMARK EVALUATION PROMPTS We reproduce the four prompt templates for evaluating generated slides: Instruction Following with a textual style description, Instruction Following with a structured Style JSON specification, Aes- thetic Quality, and visual Content Faithfulness, together with the two edit-evaluator prompts: one for Editing Success and Overediting (reported as Instruction Following and Edit Consistency in Section 4.3) and one for Presentation Quality. Each template is reproduced in full, including its evaluation procedure and JSON output format. The style specification and images for each exam- ple are supplied separately to the evaluator. The IF and AQ templates also ask the judge for an overall_score; we do not use that field and report the unweighted means of the per-dimension 20 Preprint scores (Appendix C.1). The image-preservation template returns a single overall_score, which we use directly. The Content Faithfulness template below evaluates the preservation of required input images in multi-image grounded generation. Text-rendering fidelity is computed separately using the OCR- based score in Equation 2. The edit evaluator receives the source and edited slides for its first prompt, and the edited slide alone for its presentation-quality prompt. The resulting scores are aggregated as described in Section 4.3. B.5.1INSTRUCTION FOLLOWING: TEXTUAL STYLE DESCRIPTION The evaluator receives a textual style description and the generated slide image, optionally accom- panied by a reference slide that provides visual style context. You are an expert evaluator for AI-generated presentation slides. Your task is to assess how well a generated slide image follows the style instructions defined in a descriptive style specification text. Your default stance is skeptical and conservative. Start from strict compliance assumptions and deduct for every mismatch. Do not reward visual attractiveness. ## Evaluation Dimension: Style Instruction Following This dimension evaluates whether the generated slide image faithfully follows the given style specification. The focus is NOT on whether the result looks beautiful, but on whether it was made according to the requirements. The evaluation MUST be based on strict comparison with the style description text. Visual appeal or subjective quality MUST NOT influence the score. Even small deviations from the specification should be penalized. ## Inputs 1. ** Style Description Text ** : A descriptive text specification that defines the visual style requirements for the slide, potentially including: - Theme name, background style (color, gradient, texture, pattern), font palette, color palette, and special instructions - Mood/tone and composition style - Block-level style requirements (color, font, position, container, text effects, etc.) 2. ** Generated Slide Image ** : The AI-generated slide image that should conform to the style description text. 3. ** Reference Slide Image(s) [optional] ** : One slide from the same presentation, generated earlier in the same deck. Use these only to infer the visual style of the presentation, such as palette, typography, background treatment, decorative motifs, and overall aesthetic language. Do NOT use them to infer or judge the target slide’s layout, block positions, or content placement unless those details are explicitly present in the style description text. You MUST explicitly check each requirement in the style description text and verify whether it is present in the generated slide. Do NOT assume correctness without visual evidence. You MUST perform requirement-by-requirement verification before scoring. If an item cannot be clearly verified from the image, treat it as not satisfied. ## Mandatory Evaluation Procedure Follow this exact order: 1. Extract all checkable requirements from the style description text (global + hierarchy + block-level). 2. For each requirement, assign one status: PASS / PARTIAL / FAIL / NOT_VERIFIABLE. 3. Count mismatches. Any PARTIAL, FAIL, or NOT_VERIFIABLE counts as a mismatch. 4. Apply hard score caps first (see Strict Scoring Rules), then assign sub-dimension scores. 5. Compute the overall score as the mean of the sub-dimension scores. Never skip steps. Never score first and justify later. ## Evaluation Criteria 21 Preprint Evaluate the following sub-dimensions carefully: ### 1. Background Compliance - Does the background color match the specified hex code? - If a gradient is specified, does the background show the correct gradient direction and colors? - Are specified textures (paper, noise, grid, dots) or patterns present? ### 2. Color Palette Adherence - Does the slide use the specified primary, secondary, text, and accent colors? - Are the color relationships (primary vs secondary vs accent) maintained? - Do text elements use the specified text color? ### 3. Typography Compliance - Does the font style match the specified font families (serif vs sans-serif, bold vs regular)? - Is the font weight (bold/normal/light) correctly applied? - Are text effects (shadow, outline, glow) applied as specified? ### 4. Layout & Position Accuracy - Are elements positioned as specified (e.g., "top-left", "center", "bottom-right")? - Does the composition style match (e.g., centered, two-column, asymmetric, grid-based)? - Is the visual hierarchy maintained (dominant elements are larger/more prominent)? ### 5. Mood & Atmosphere Match - Does the overall mood match the specified mood_tone (e.g., Professional, Technical, Playful)? - Are global style instructions in the style description text followed? - Do decorative elements and visual embellishments match the description? ### 6. Block-Level Style Fidelity - For each block, are the specified style attributes (container, opacity, and any block-level special instructions) followed? - Are illustration blocks rendered according to their natural language descriptions? - Do image containers have the specified effects (border, shadow, frame)? ## Strict Scoring Rules - Any clear mismatch with the style description text (e.g., wrong color, incorrect layout, missing effect) MUST result in a score of 6 or below for that sub-dimension. - Multiple mismatches in one dimension MUST result in a score of 4 or below. - If a key requirement (e.g., background type, layout structure, primary color) is missing or incorrect, the score for that dimension MUST NOT exceed 5. - Perfect or near-perfect scores (9-10) are ONLY allowed when there are NO noticeable deviations. - If uncertain, always choose the lower score. Additional hard constraints (must enforce): - A sub-dimension can be 9-10 only if ALL its checkable requirements are PASS and zero mismatches exist. - A sub-dimension can be at most 8 if there is any PARTIAL. - A sub-dimension can be at most 6 if there is any FAIL. - A sub-dimension can be at most 5 if any key requirement in that dimension is FAIL or NOT_VERIFIABLE. - A sub-dimension can be at most 4 if mismatch count in that dimension is 2 or more. - A sub-dimension can be at most 3 if mismatch count in that dimension is 3 or more. - Do not give score 7-10 when evidence is weak, ambiguous, occluded, or not visible. ## Scoring Rubric - ** 9-10 (Excellent) ** : Nearly perfect match. NO visible mismatches in color, layout, typography, or mood. - ** 7-8 (Good) ** : Mostly correct, but includes noticeable deviations that affect fidelity. - ** 5-6 (Fair) ** : Multiple deviations or one major mismatch in key style elements. - ** 3-4 (Poor) ** : Significant mismatches in several dimensions; style is only partially recognizable. - ** 1-2 (Very Poor) ** : Style instructions are largely ignored or contradicted. Scoring bias control: - Do not use score 8 as a default "safe" score. 22 Preprint - Prefer 5-7 unless there is strong evidence for higher. - Aesthetic quality, creativity, readability, and beauty must not raise the score if fidelity is weak. - If two score options seem plausible, choose the lower one. ## Output Format You MUST respond in the following JSON format ONLY. Do not include any other text: ‘json "background_compliance": "score": <1-10>, "reasoning": "<brief explanation>" , "color_palette_adherence": "score": <1-10>, "reasoning": "<brief explanation>" , "typography_compliance": "score": <1-10>, "reasoning": "<brief explanation>" , "layout_position_accuracy": "score": <1-10>, "reasoning": "<brief explanation>" , "mood_atmosphere_match": "score": <1-10>, "reasoning": "<brief explanation>" , "block_level_style_fidelity": "score": <1-10>, "reasoning": "<brief explanation>" , "overall_score": <1-10 mean of the sub-dimension scores>, "overall_reasoning": "<2-3 sentence summary of the overall style instruction following quality>" ‘ Reasoning must be evidence-grounded and mention concrete mismatches (for example: wrong hex family, missing texture, incorrect block position, missing effect, wrong hierarchy emphasis). PROMPT 1INSTRUCTION FOLLOWING / TEXT B.5.2INSTRUCTION FOLLOWING: STRUCTURED STYLE JSON This variant receives a structured Style JSON specification in place of the textual description, to- gether with the generated slide image and optional reference slide. You are an expert evaluator for AI-generated presentation slides. Your task is to assess how well a generated slide image follows the style instructions defined in a structured Style JSON specification. Your default stance is skeptical and conservative. Start from strict compliance assumptions and deduct for every mismatch. Do not reward visual attractiveness. ## Evaluation Dimension: Style Instruction Following This dimension evaluates whether the generated slide image faithfully follows the given style specification. The focus is NOT on whether the result looks beautiful, but on whether it was made according to the requirements. The evaluation MUST be based on strict comparison with the Style JSON. Visual appeal or subjective quality MUST NOT influence the score. Even small deviations from the specification should be penalized. ## Inputs 1. ** Style JSON ** : A structured JSON specification that defines the visual style requirements for the slide, including: 23 Preprint - ‘global_style‘: Theme name, background (color, gradient, texture, pattern), font palette, color palette, and special instructions - ‘visual_hierarchy‘: Mood/tone and composition style - ‘styled_slides[].blocks‘: Individual content blocks with their style attributes (color, font, position, container, text effects, etc.) 2. ** Generated Slide Image ** : The AI-generated slide image that should conform to the Style JSON specification. 3. ** Reference Slide Image(s) [optional] ** : One slide from the same presentation, generated earlier in the same deck. Use these only to infer the visual style of the presentation, such as palette, typography, background treatment, decorative motifs, and overall aesthetic language. Do NOT use them to infer or judge the target slide’s layout, block positions, or content placement unless those details are explicitly present in the Style JSON. You MUST explicitly check each requirement in the Style JSON and verify whether it is present in the generated slide. Do NOT assume correctness without visual evidence. You MUST perform requirement-by-requirement verification before scoring. If an item cannot be clearly verified from the image, treat it as not satisfied. ## Mandatory Evaluation Procedure Follow this exact order: 1. Extract all checkable requirements from Style JSON (global + hierarchy + block-level). 2. For each requirement, assign one status: PASS / PARTIAL / FAIL / NOT_VERIFIABLE. 3. Count mismatches. Any PARTIAL, FAIL, or NOT_VERIFIABLE counts as a mismatch. 4. Apply hard score caps first (see Strict Scoring Rules), then assign sub-dimension scores. 5. Compute the overall score as the mean of the sub-dimension scores. Never skip steps. Never score first and justify later. ## Evaluation Criteria Evaluate the following sub-dimensions carefully: ### 1. Background Compliance - Does the background color match the specified hex code? - If a gradient is specified, does the background show the correct gradient direction and colors? - Are specified textures (paper, noise, grid, dots) or patterns present? ### 2. Color Palette Adherence - Does the slide use the specified primary, secondary, text, and accent colors? - Are the color relationships (primary vs secondary vs accent) maintained? - Do text elements use the specified text color? ### 3. Typography Compliance - Does the font style match the specified font families (serif vs sans-serif, bold vs regular)? - Is the font weight (bold/normal/light) correctly applied? - Are text effects (shadow, outline, glow) applied as specified? ### 4. Layout & Position Accuracy - Are elements positioned as specified (e.g., "top-left", "center", "bottom-right")? - Does the composition style match (e.g., centered, two-column, asymmetric, grid-based)? - Is the visual hierarchy maintained (dominant elements are larger/more prominent)? ### 5. Mood & Atmosphere Match - Does the overall mood match the specified mood_tone (e.g., Professional, Technical, Playful)? - Are special instructions in the global_style followed? - Do decorative elements and visual embellishments match the description? ### 6. Block-Level Style Fidelity - For each block, are the specified style attributes (container, opacity, special_instructions) followed? - Are illustration blocks rendered according to their natural language descriptions? - Do image containers have the specified effects (border, shadow, frame)? 24 Preprint ## Strict Scoring Rules - Any clear mismatch with the Style JSON (e.g., wrong color, incorrect layout, missing effect) MUST result in a score of 6 or below for that sub-dimension. - Multiple mismatches in one dimension MUST result in a score of 4 or below. - If a key requirement (e.g., background type, layout structure, primary color) is missing or incorrect, the score for that dimension MUST NOT exceed 5. - Perfect or near-perfect scores (9-10) are ONLY allowed when there are NO noticeable deviations. - If uncertain, always choose the lower score. Additional hard constraints (must enforce): - A sub-dimension can be 9-10 only if ALL its checkable requirements are PASS and zero mismatches exist. - A sub-dimension can be at most 8 if there is any PARTIAL. - A sub-dimension can be at most 6 if there is any FAIL. - A sub-dimension can be at most 5 if any key requirement in that dimension is FAIL or NOT_VERIFIABLE. - A sub-dimension can be at most 4 if mismatch count in that dimension is 2 or more. - A sub-dimension can be at most 3 if mismatch count in that dimension is 3 or more. - Do not give score 7-10 when evidence is weak, ambiguous, occluded, or not visible. ## Scoring Rubric - ** 9-10 (Excellent) ** : Nearly perfect match. NO visible mismatches in color, layout, typography, or mood. - ** 7-8 (Good) ** : Mostly correct, but includes noticeable deviations that affect fidelity. - ** 5-6 (Fair) ** : Multiple deviations or one major mismatch in key style elements. - ** 3-4 (Poor) ** : Significant mismatches in several dimensions; style is only partially recognizable. - ** 1-2 (Very Poor) ** : Style instructions are largely ignored or contradicted. Scoring bias control: - Do not use score 8 as a default "safe" score. - Prefer 5-7 unless there is strong evidence for higher. - Aesthetic quality, creativity, readability, and beauty must not raise the score if fidelity is weak. - If two score options seem plausible, choose the lower one. ## Output Format You MUST respond in the following JSON format ONLY. Do not include any other text: ‘json "background_compliance": "score": <1-10>, "reasoning": "<brief explanation>" , "color_palette_adherence": "score": <1-10>, "reasoning": "<brief explanation>" , "typography_compliance": "score": <1-10>, "reasoning": "<brief explanation>" , "layout_position_accuracy": "score": <1-10>, "reasoning": "<brief explanation>" , "mood_atmosphere_match": "score": <1-10>, "reasoning": "<brief explanation>" , "block_level_style_fidelity": "score": <1-10>, "reasoning": "<brief explanation>" , "overall_score": <1-10 mean of the sub-dimension scores>, 25 Preprint "overall_reasoning": "<2-3 sentence summary of the overall style instruction following quality>" ‘ Reasoning must be evidence-grounded and mention concrete mismatches (for example: wrong hex family, missing texture, incorrect block position, missing effect, wrong hierarchy emphasis). PROMPT 2INSTRUCTION FOLLOWING / STYLE JSON B.5.3AESTHETIC QUALITY This evaluator assesses the generated slide image without a target style specification. You are an expert evaluator for AI-generated presentation slides. Your task is to assess the Aesthetic Quality of a generated slide image. Your default stance is skeptical and conservative. Start from the assumption that the slide is NOT professional unless clear visual evidence proves otherwise. Do not reward creativity, attractiveness, or stylistic ambition unless execution quality is objectively strong. ## Evaluation Dimension: Aesthetic Quality This dimension evaluates whether the generated slide looks like a professional, readable, structurally coherent, and realistically usable PPT page. The focus is on visual communication effectiveness, layout discipline, readability, consistency, and production-level polish. Aesthetic appeal alone MUST NOT increase the score. ## Inputs 1. ** Generated Slide Image ** : The AI-generated slide image to be evaluated. ## Mandatory Evaluation Procedure Follow this exact order: 1. Inspect the entire slide globally before scoring. 2. Extract all observable visual qualities and defects related to readability, spacing, alignment, consistency, hierarchy, and presentation polish. 3. For each sub-dimension, explicitly identify: - strengths - weaknesses - visible defects 4. Count all visible issues. Any noticeable issue counts as a mismatch. 5. Apply hard score caps before assigning scores. 6. Compute the overall score as the mean of the sub-dimension scores. Never score first and justify later. If a feature is ambiguous, partially visible, blurry, cropped, distorted, or difficult to verify, treat it as a defect rather than assuming correctness. ## Evaluation Criteria Evaluate the following sub-dimensions carefully: ### 1. Clarity Evaluate readability and information communication quality. Check: - Are all text elements clearly legible? - Is font size sufficient for presentation viewing? - Is contrast between text and background adequate? - Is the hierarchy between title, subtitle, body text, captions, and highlights visually obvious? - Is reading order natural and effortless? - Are charts, icons, diagrams, and images recognizable and not blurry? 26 Preprint - Is information density appropriate? Penalize: - Small or crowded text - Low contrast - Visual noise - Overlapping elements - Ambiguous hierarchy - Cluttered composition - Illegible charts/tables - Excessive decoration that harms readability ### 2. Layout Evaluate structural organization and spatial discipline. Check: - Are elements aligned consistently? - Is spacing between blocks visually even and intentional? - Is whitespace used effectively? - Is the composition balanced? - Are margins clean and professional? - Is the slide visually stable rather than chaotic? - Are elements properly grouped? Penalize: - Misalignment - Uneven spacing - Cramped layout - Excessive empty space - Floating or disconnected elements - Poor balance - Edge crowding - Inconsistent sizing - Random placement ### 3. Style Coherence Evaluate visual consistency and design system coherence. Check: - Are colors used consistently? - Are typography choices coherent? - Do containers/cards/shapes follow a unified style? - Are illustration styles consistent? - Are icon styles visually compatible? - Is decoration applied consistently across blocks? - Does the slide feel like one unified design system? Penalize: - Mixed visual styles - Inconsistent fonts - Random accent colors - Incompatible iconography - Uneven border/shadow usage - Inconsistent image treatment - Decorative elements that conflict stylistically - Abrupt style changes between blocks ### 4. Presentation Professionalism Evaluate whether the slide resembles a realistic, production-quality professional PPT. Check: - Does the slide look polished and presentation-ready? - Would the slide be acceptable in a business, academic, investor, consulting, or conference setting? - Is the visual quality comparable to professionally designed slides? - Does the slide avoid common AI-generation artifacts? - Does the slide appear intentional rather than auto-generated? Penalize: - Obvious AI artifacts - Distorted text or shapes - Unrealistic layout behavior - Amateur styling 27 Preprint - Excessive gimmicks - Poor typography discipline - Inconsistent rendering quality - Visually cheap or template-like appearance - Lack of refinement ## Strict Scoring Rules You MUST apply these hard constraints strictly. ### General Rules - Every visible issue counts as a mismatch. - Small defects still require deduction. - Do NOT assume missing details are correct. - If uncertain between two scores, choose the lower one. - Aesthetic attractiveness MUST NOT compensate for structural or readability flaws. ### Sub-dimension Caps - Score 9-10 ONLY if there are essentially no noticeable flaws. - Score 8 is allowed only for very minor defects. - Any clearly noticeable defect caps the sub-dimension at 7. - Two or more noticeable defects cap the sub-dimension at 6. - Any major readability/layout/professionalism issue caps the sub-dimension at 5. - Multiple major issues cap the sub-dimension at 4 or below. - Severe readability failure MUST score 3 or below. ### Automatic Major Defects The following are considered major defects: - Illegible or blurry text - Overlapping content - Broken hierarchy - Chaotic layout - Severe alignment inconsistency - Strong visual clutter - Distorted charts/icons - Mixed incompatible styles - Obvious AI rendering artifacts - Unprofessional appearance ## Scoring Rubric - ** 9-10 (Excellent) ** : Extremely polished, highly readable, visually disciplined, and indistinguishable from a professionally designed presentation slide. No meaningful flaws. - ** 7-8 (Good) ** : Strong overall quality, but includes noticeable issues in spacing, hierarchy, consistency, or polish. - ** 5-6 (Fair) ** : Moderately effective but contains multiple visible weaknesses that reduce professionalism or readability. - ** 3-4 (Poor) ** : Significant design and communication problems. Slide appears unrefined or structurally weak. - ** 1-2 (Very Poor) ** : Severely unreadable, chaotic, inconsistent, or obviously broken. Does not resemble a usable professional PPT slide. ## Scoring Bias Control - Do not use 8 as a default “safe” score. - Prefer 5-7 unless evidence strongly supports higher quality. - Do not reward effort, complexity, or decoration. - Professional simplicity should score higher than decorative clutter. - Conservative scoring is preferred. ## Output Format You MUST respond in the following JSON format ONLY. Do not include any other text: ‘json "clarity": "score": <1-10>, "reasoning": "<brief explanation with concrete evidence>" , "layout": "score": <1-10>, 28 Preprint "reasoning": "<brief explanation with concrete evidence>" , "style_coherence": "score": <1-10>, "reasoning": "<brief explanation with concrete evidence>" , "presentation_professionalism": "score": <1-10>, "reasoning": "<brief explanation with concrete evidence>" , "overall_score": <1-10 mean of the sub-dimension scores>, "overall_reasoning": "<2-3 sentence summary grounded in visible strengths and defects>" PROMPT 3AESTHETIC QUALITY B.5.4CONTENT FAITHFULNESS: REQUIRED INPUT IMAGES The evaluator compares the generated slide with the required input images. The template retains the original terms “Generated PPT” and “Sub-Image List” for these inputs. You are an expert evaluator for AI-generated presentation slides. Your task is to assess ** Content Faithfulness ** by rigorously verifying whether all required sub-images in a provided sub-image list are correctly represented in the generated PPT. Your default stance is skeptical and conservative. Do NOT assume a sub-image is correctly matched unless there is strong visual evidence. Partial similarity is NOT sufficient for full credit. The focus is strict faithfulness to required content, NOT aesthetic quality. ## Evaluation Target Evaluate whether every required sub-image (content block) from the sub-image list appears in the generated PPT with: - correct semantic content - correct visual identity - correct structural composition - reasonable positional/layout consistency Each sub-image may represent: - charts - tables - figures - diagrams - screenshots - icon groups - text blocks - mixed visual compositions You MUST verify each required sub-image individually before assigning scores. ## Inputs 1. ** Generated PPT ** : The AI-generated presentation file to be evaluated. 2. ** Sub-Image List ** : A list of required sub-images that must appear in the generated PPT. ## Mandatory Evaluation Procedure Follow this exact order: 1. Enumerate all required sub-images from the sub-image list. 2. For EACH required sub-image: - search for the best matching region/page in the generated PPT - determine whether it is: - EXACT_MATCH - PARTIAL_MATCH 29 Preprint - FAIL - NOT_FOUND 3. Explicitly verify: - semantic content - major text - chart/table meaning - visual composition - structural arrangement 4. Count mismatches: - PARTIAL_MATCH counts as a mismatch - FAIL counts as a mismatch - NOT_FOUND counts as a major mismatch 5. Apply hard score caps before assigning the final score. 6. Compute final score conservatively. Never score first and justify later. If content is blurry, ambiguous, cropped, occluded, distorted, or difficult to verify, treat it as NOT fully matched. ## Evaluation Criteria ### 1. Coverage Completeness Check whether ALL required sub-images are present. Requirements: - Every required sub-image must appear somewhere in the PPT. - Presence alone is NOT enough; the matched region must clearly correspond to the intended content block. - Missing required sub-images are major failures. Penalize: - Missing blocks - Replaced blocks - Hallucinated substitute content - Severe cropping - Wrong slide/page placement if it breaks intended correspondence ### 2. Content Correctness Check whether matched sub-images preserve the intended semantic content. Verify: - Main text content - Key labels - Chart type and meaning - Table structure and values (when visible) - Diagram semantics - Important icons/symbols - Core visual identity Penalize: - Wrong chart type - Incorrect labels - Missing important elements - Hallucinated text - Semantic mismatch - Incorrect data relationships - Wrong figure identity - Fake or placeholder content Superficial resemblance is NOT sufficient. ### 3. Visual / Structural Consistency Check whether the matched content block is structurally consistent with the required sub-image. Verify: - Overall composition - Relative arrangement of elements - Element grouping - Layout pattern - Visual hierarchy - Spatial organization 30 Preprint Minor stylistic differences are acceptable ONLY if the semantic and structural identity remain clearly faithful. Penalize: - Major layout restructuring - Missing key visual regions - Incorrect element arrangement - Collapsed or merged structures - Broken hierarchy - Distorted composition ## Strict Scoring Rules You MUST apply these hard constraints strictly. ### General Principles - Any missing required sub-image is a major error. - Any ambiguous match should be downgraded. - Similar-looking but semantically incorrect content is a FAIL. - Do NOT infer correctness from approximate appearance alone. - If uncertain between two scores, choose the lower score. ### Match-Level Interpretation - EXACT_MATCH: - Correct semantic content - Correct structural composition - No meaningful missing elements - PARTIAL_MATCH: - Some recognizable correspondence exists - But important content/structure differs or is missing - FAIL: - Wrong semantic content - Wrong structure - Hallucinated replacement - Severe distortion - NOT_FOUND: - Required sub-image cannot be reliably identified anywhere in the PPT ### Hard Score Caps - Any NOT_FOUND item caps overall_score at 6. - Two or more NOT_FOUND items cap overall_score at 4. - Any major semantic mismatch caps overall_score at 5. - Multiple FAIL items cap overall_score at 4. - If more than 30% of sub-images are PARTIAL_MATCH or worse, overall_score MUST NOT exceed 6. - If more than 50% are PARTIAL_MATCH or worse, overall_score MUST NOT exceed 4. - Perfect scores (9-10) are ONLY allowed when ALL required sub-images are present with near-perfect semantic and structural fidelity. ### Severity Guidelines Major errors include: - Missing required block - Wrong chart/table/diagram identity - Incorrect semantic meaning - Hallucinated replacement content - Major structural mismatch - Severe cropping or truncation Minor errors include: - Small local visual deviations - Slight spacing/layout changes - Minor style differences - Small non-critical omissions ## Scoring Rubric (1-10) - ** 9-10 (Excellent) ** : All required sub-images are present with highly accurate semantic and structural fidelity. No meaningful mismatches. - ** 7-8 (Good) ** : Most required sub-images are correct, but some minor omissions or structural inconsistencies exist. - ** 5-6 (Fair) ** : Noticeable missing, partial, or incorrect sub-images reduce overall faithfulness. 31 Preprint - ** 3-4 (Poor) ** : Many required sub-images are missing, structurally wrong, or semantically incorrect. - ** 1-2 (Very Poor) ** : Most required content is absent, hallucinated, or fundamentally mismatched. ## Scoring Bias Control - Do NOT use score 8 as a default safe score. - Prefer conservative scoring. - Similar appearance alone MUST NOT receive high scores. - Semantic correctness is more important than visual attractiveness. - Presence alone is insufficient without content fidelity. - If evidence is weak or unclear, lower the score. ## Output Format You MUST respond in the following JSON format ONLY. Do not include any other text: ‘json "overall_score": <1-10>, "overall_reasoning": "<2-4 sentence summary explicitly discussing missing sub-images, semantic mismatches, structural inconsistencies, and overall faithfulness quality>" PROMPT 4CONTENT FAITHFULNESS B.5.5EDIT EVALUATION: EDITING SUCCESS AND OVEREDITING The edit evaluator compares the source slide and edited slide, together with the natural-language instruction, and returns Editing Success and Overediting scores. The complete evaluation prompt is shown below. You are an expert evaluator for instruction-guided slide image editing. All images are AI-generated slide pages. Focus on the editing result itself. IMPORTANT: Return exactly one JSON object in this format: "reasoning": "...", "score": [...] Keep the reasoning concise. RULES: Two images will be provided: the first is the source slide and the second is the edited slide. Evaluate whether the edit instruction is correctly applied while preserving all content that should remain unchanged. Use a 0 to 10 scale for both scores: 1. Editing Success: 0 means the instruction is not followed at all, 10 means the requested slide edit is executed perfectly. 2. Overediting: 0 means the slide is unnecessarily changed or damaged, 10 means only the intended parts are changed and the rest is preserved well. Return: "reasoning": "...", "score": [editing_success, overediting] Instruction: <instruction> PROMPT 5AEDIT EVALUATION / SUCCESS + PRESERVATION 32 Preprint B.5.6EDIT EVALUATION: PRESENTATION QUALITY The second edit-evaluation pass judges the edited slide itself on clarity, layout, style coherence, and presentation professionalism. The complete evaluation prompt is shown below. You are an expert evaluator for instruction-guided slide image editing. All images are AI-generated slide pages. Focus on the editing result itself. IMPORTANT: Return exactly one JSON object in this format: "reasoning": "...", "score": [...] Keep the reasoning concise. RULES: One edited slide image will be provided. Evaluate the presentation quality of the edited slide page itself using a 0 to 10 scale for each dimension: 1. Clarity: text/chart readability, hierarchy, and reading order. 2. Layout: alignment, spacing, composition balance, and use of whitespace. 3. Style Coherence: consistency of colors, fonts, decoration, and image style. 4. Presentation Professionalism: whether the page looks polished and like a real professional slide. Return: "reasoning": "...", "score": [clarity, layout, style_coherence, presentation_professionalism] PROMPT 5BEDIT EVALUATION / PRESENTATION QUALITY CEXPERIMENTAL DETAILS AND ADDITIONAL RESULTS C.1EXPERIMENTAL SETUP DETAILS Training configuration. Stage 1 continual pretraining uses text-to-slide pairs for 13,500 steps with a global batch size of 384. Stage 2 uses a mixture of all four tasks for 16,500 steps with a global batch size of 640 and at most four conditioning images per example. The T2I and template- conditioned SFT runs use a global batch size of 640; the multi-image and editing runs use 768. Text inputs are capped at 4,096 tokens throughout. We optimize the diffusion transformer with AdamW (β 1 = 0.9, β 2 = 0.95, ε = 10 −8 , weight decay 0.01, gradient-norm clipping at 1.0) at a learning rate of 5×10 −5 for both continual pretraining and SFT, keeping the Qwen3 text encoder and VAE frozen. Stage 1 warms up over 500 steps from 10 −7 ; later runs use a 20-step warmup. Training uses BF16 mixed precision, gradient checkpointing, FSDP with hybrid sharding, one sample per GPU with eight gradient-accumulation steps, and seed 2233. Inputs are resized with aspect ratio preserved to an area of at most 1024 2 pixels and a longest side of at most 2,048 pixels, rounded down to multiples of 16, so no single aspect ratio is imposed. The final model is a linear interpolation of the four task checkpoints. Training resources. All runs use nodes of eight NVIDIA H100 80GB GPUs (Table 11). SFT runs are step-based; in exposure-equivalent epochs (steps × global batch / training examples), the shared T2I SFT covers 1.1 epochs; the T2I branch continues the same run for 3,000 further steps (2.2 epochs), and the template-conditioned, multi-image, and editing branches cover 3.2, 3.3, and 2.8 epochs. Inference configuration. InfoVis uses the FlowMatchEulerDiscreteScheduler with 50 denoising steps and guidance scale 4.0 in BF16. Text-to-slide outputs are 1280× 720, template-conditioned and multi-image grounded outputs are 1376× 768, and editing outputs match the size of the source slide. The text context is 4,096 tokens, and longer prompts are truncated by tokenization without 33 Preprint Table 11: Training stages and resources. The shared checkpoint θ shared is the step-1,500 checkpoint of the T2I SFT run. RunNodesGPUsStepsGlobal batch Stage 1: T2I CPT64813,500384 Stage 2: four-task mixed CPT108016,500640 T2I SFT10804,500640 Template-conditioned SFT10801,500640 Multi-image grounded SFT12962,500768 Editing SFT1296500768 rewriting. The random seed for each example is a base seed plus the example index; the best-of-8 experiments use the base seeds2233, 3344, 4455, 5566, 6677, 7788, 8899, 9900. Verifier-guided test-time scaling. We generate K = 8 candidates per prompt and report best- of-K for K ∈ 2, 4, 8, with K = 1 the single-sample result. Each candidate is scored four times by a Qwen3.5-27B (Qwen Team, 2026a) slide-quality verifier, and the mean score is used for selection (Luo et al., 2026). The aesthetic verifier prompt returns a weighted 1–10 score over clarity (30%), layout (25%), style coherence (20%), and presentation professionalism (25%); the instruction-following verifier uses separate prompts for free-form style descriptions and structured style_json. The verifier only selects among candidates and never updates InfoVis. Comparison models. The proprietary baselines are GPT-Image-2 (OpenAI, 2026), Nano-Banana- 2 (Google, 2026), and Qwen-Image-2 (Qwen Team, 2026b). The open-source baselines are Qwen- Image and Qwen-Image-Edit (Wu et al., 2025) (the Qwen-Image-Edit-2509 release), Z-Image- Turbo (Z-Image Team, 2025), LongCat-Image and LongCat-Image-Edit (Meituan LongCat Team et al., 2025) (the Turbo release), FLUX.2-dev (Black Forest Labs, 2025) and FLUX.2-klein-base- 9B (Black Forest Labs, 2026), HiDream-O1-Image (Cai et al., 2026), BAGEL (Deng et al., 2025), and OmniGen2 (Wu et al., 2026). Evaluator configuration. The GPT-4.1 evaluator is called with temperature 0.2 and a 2,000-token output limit for Instruction Following and Aesthetic Quality, with one judge call per image. Content Faithfulness uses the same model and temperature, with 800 output tokens for extracting the target text from each instruction and 1,200 for the image-preservation judgment in multi-image grounded generation. OCR uses PaddleOCR 3.3.2 with document-orientation classification and text-line ori- entation enabled. Editing scores use GPT-4.1 with temperature 0.7 and a 512-token output limit, one call per prompt. The reported IF and AQ scores are the unweighted means of the per-dimension scores returned by the judge, recomputed outside the judge; for IF and AQ the overall_score field the judge returns is not used. The image-preservation score in multi-image CF is the single overall_score its evaluator returns. All rows of Tables 1 and 4 use this aggregation. The paired bootstrap cited in Section 6.2 draws 10,000 resamples of benchmark cases within each task split, over the cases scored for both models, and reports 95% percentile intervals: InfoVis is 0.06 below Nano-Banana-2 overall on these cases (CI 0.01–0.10), with no detectable difference on the nine generation metrics (CI−0.02 to 0.07), and 0.42 below GPT-Image-2 overall (CI 0.38–0.46). C.2DETAILED GENERATION AND EDITING RESULTS Tables 12–14 report the detailed results for the three generation tasks, and Table 15 reports the nine editing operations. These task averages weight the listed dimensions equally. They therefore use a different aggregation from the eleven group-level metrics in the main comparison (Table 1). C.3SOURCE-STRATIFIED EVALUATION VisSlideBench combines records derived from real presentation decks with synthetic records gen- erated during benchmark construction: 167 real and 150 synthetic text-to-slide records, 147 and 150 template-conditioned records, and 179 and 150 multi-image records. We recompute the three generation-task scores on each partition. For each slide, the score is the arithmetic mean of CF, IF, 34 Preprint Table 12: Detailed text-to-slide results. CF reports Text Rendering. IF reports Background Compli- ance (BG), Color Palette Adherence (Color), Typography Compliance (Typo), Layout and Position Accuracy (Layout), Mood and Atmosphere Match (Mood), and Block-Level Style Fidelity (Block). AQ reports Clarity, Layout Quality (LayoutQ), Style Coherence (StyleQ), and Presentation Profes- sionalism (Prof). Overall averages these eleven dimensions. Bold marks the best score within each model group. Higher is better. TypeModel CFIFAQ Overall TextBGColorTypoLayoutMoodBlockClarityLayoutQStyleQProf Proprietary GPT-Image-28.528.718.448.318.449.268.238.057.298.097.398.25 Nano-Banana-28.388.398.137.817.978.937.817.726.977.666.977.89 Qwen-Image-27.187.016.906.726.837.596.296.326.346.975.886.73 Open-source Qwen-Image6.507.096.705.856.047.045.344.845.385.924.555.93 Z-Image-Turbo6.667.727.166.516.457.495.685.256.186.734.996.44 LongCat-Image4.696.916.164.855.055.883.973.564.615.263.574.96 FLUX.2-dev6.608.247.566.466.777.745.704.205.936.654.386.38 HiDream-O1-Image6.825.975.835.255.646.204.854.504.815.324.095.39 BAGEL3.507.506.644.415.685.834.032.524.675.242.744.80 OmniGen23.896.836.014.194.805.313.652.383.754.032.414.30 InfoVis (ours)8.428.327.987.627.818.787.537.346.717.426.607.68 Table 13: Detailed template-conditioned generation results. Dimensions follow Table 12; Overall averages the same eleven dimensions. Bold marks the best score within each model group. Higher is better. TypeModel CFIFAQ Overall TextBGColorTypoLayoutMoodBlockClarityLayoutQStyleQProf Proprietary GPT-Image-28.199.088.708.507.519.347.937.937.228.027.328.16 Nano-Banana-27.598.858.267.976.748.906.967.386.737.476.747.60 Qwen-Image-28.236.656.496.075.896.935.685.855.536.245.356.26 Open-source Qwen-Image-Edit3.107.257.026.514.466.633.985.195.496.114.835.51 LongCat-Image-Edit4.867.086.515.695.466.704.524.515.375.884.415.54 FLUX.2-dev6.288.847.966.856.578.275.774.125.896.564.306.49 HiDream-O1-Image5.974.965.445.085.535.544.304.264.715.043.974.98 BAGEL3.316.456.043.965.035.623.902.654.444.982.854.48 OmniGen23.757.976.984.525.156.284.042.483.974.312.594.73 InfoVis (ours)8.148.858.247.877.298.947.337.256.727.416.627.70 Table 14: Detailed multi-image grounded generation results. In addition to Text Rendering, CF includes Multi-image Consistency (MIC), which measures preservation and use of supplied assets. Other dimensions follow Table 12. Overall averages all twelve dimensions. Bold marks the best score within each model group. Higher is better. TypeModel CFIFAQ Overall TextMICBGColorTypoLayoutMoodBlockClarityLayoutQStyleQProf Proprietary GPT-Image-28.389.478.748.298.268.209.168.007.837.248.067.308.24 Nano-Banana-27.749.468.377.877.657.528.787.397.346.777.666.797.78 Qwen-Image-27.256.696.426.316.235.976.815.755.505.446.185.076.14 Open-source Qwen-Image-Edit3.744.945.965.533.964.205.373.403.414.415.273.394.47 LongCat-Image-Edit4.585.915.975.794.604.595.763.914.074.675.433.884.93 FLUX.2-dev6.486.608.447.546.546.727.845.694.295.766.404.346.39 HiDream-O1-Image5.795.415.325.174.724.765.384.053.904.384.763.644.77 BAGEL3.224.265.855.353.804.745.073.752.674.094.642.814.19 OmniGen23.674.456.355.713.894.595.273.602.473.744.162.534.20 InfoVis (ours)8.229.208.457.877.747.708.807.497.216.787.576.677.81 35 Preprint Table 15: Results for all nine editing operations. Text, Typo, Image, Theme, and BG denote text modification, typography changes, image replacement, theme switching, and background updates. Each score is the geometric mean of Semantic Consistency (SC) and Presentation Quality (PQ); Overall uses overall SC and PQ. Bold marks the best score within each model group. Higher is better. TypeModelTextColorFontTypoRemoveAddImageThemeBGOverall Proprietary GPT-Image-29.038.838.528.648.428.989.448.778.748.88 Nano-Banana-28.938.808.308.538.078.859.339.268.628.83 Qwen-Image-25.825.673.844.542.806.156.437.755.795.66 Open-source Qwen-Image-Edit7.026.265.956.807.667.958.748.137.847.57 LongCat-Image-Edit5.526.584.065.227.236.667.957.416.946.63 FLUX.2-dev6.045.994.424.685.845.918.227.888.256.63 HiDream-O1-Image2.161.341.342.920.742.363.945.751.402.90 BAGEL3.315.743.814.994.135.047.276.726.495.61 OmniGen21.833.593.832.003.043.264.695.833.623.84 InfoVis (ours)8.998.728.248.468.098.348.797.948.008.41 Table 16: Generation performance by benchmark source. R and S denote real and synthetic records. Each task score averages CF, IF, and AQ over the partition; Macro averages the three task scores. |∆| is the absolute Real–Synthetic macro gap. Bold marks the best score within each model group. Higher is better except for|∆|. TypeModel T2ITemplateMulti-imageMacro |∆| RSRSRSRS Proprietary GPT-Image-28.26 8.28 8.08 8.13 8.368.28 8.23 8.23 0.004 Nano-Banana-28.00 7.92 7.37 7.70 7.957.81 7.77 7.81 0.039 Qwen-Image-26.67 6.98 7.15 6.36 5.986.60 6.60 6.65 0.046 Open-source FLUX.2-dev6.25 6.41 6.41 6.17 6.156.46 6.27 6.35 0.076 FLUX.2-klein-base-9B 6.15 6.25 6.53 6.20 6.156.47 6.28 6.31 0.029 HiDream-O1-Image5.81 5.60 5.44 4.99 4.615.22 5.28 5.27 0.017 BAGEL4.18 4.49 4.03 4.10 3.814.26 4.01 4.28 0.273 OmniGen24.03 4.08 4.37 4.23 3.924.23 4.11 4.18 0.068 InfoVis (ours)7.76 7.88 7.83 7.65 7.987.86 7.86 7.80 0.060 and AQ; we average within each task and source, and the macro score weights the three task means equally. Table 16 shows that the ranking is stable across sources. Across the nine models, the Real–Synthetic Spearman rank correlations are 1.000 for T2I, 0.983 for template-conditioned generation, 0.929 for multi-image generation, and 0.950 for the macro score. InfoVis ranks first among open-source models for every task on both sources. Its Real and Synthetic macro scores are 7.86 and 7.80, a gap of 0.060 against a mean gap of 0.068 across models. The two partitions contain different slide content, so the table shows robustness of the ranking to the source, not independence from it. C.4MULTI-IMAGE SFT BY BENCHMARK SOURCE The multi-image SFT pool is fully synthetic: its targets are rendered by Nano-Banana-2 and its conditioning assets are regions cropped from those renders (Appendix A.4). The multi-image split of VisSlideBench instead contains 179 cases from real decks and 150 synthetic cases (Appendix B.2). Table 17 reports CF, IF, and AQ on the two sources for the checkpoints of Table 4. Multi-image SFT lowers all three metrics on real cases (CF 8.64 to 7.99, IF 8.05 to 7.55, AQ 6.68 to 5.94), whereas synthetic cases lose much less (CF 8.34 to 8.29, IF 7.53 to 7.23, AQ 6.97 to 6.67) and the asset-preservation component of CF rises from 8.03 to 8.29. The typical failure on real cases is an asset that is pasted too large or redrawn, displacing or overlapping the surrounding text 36 Preprint Table 17: Multi-image grounded generation by benchmark source for the training-stage checkpoints. R: cases from real decks (179); S: synthetic cases (150). Each entry averages the per-case score over the cases with valid CF, IF, and AQ. Bold marks the best score in each column. Model Real (R)Synthetic (S) CFIFAQCFIFAQ Stage-2 CPT: four-task mixture8.607.906.348.127.116.21 Shared SFT8.648.056.688.347.536.97 Shared SFT + T2I SFT8.508.126.847.927.657.05 Shared SFT + Template SFT7.628.086.687.367.616.95 Shared SFT + Multi-image SFT7.997.555.948.297.236.67 Shared SFT + Edit SFT8.438.016.447.917.566.79 InfoVis8.798.276.908.617.707.26 (Appendix G). Template SFT, whose pool is also synthetic, lowers multi-image CF on both sources (7.62 and 7.36). Fusing the task checkpoints with those that retain the real-slide priors of continual pretraining recovers both sources, and the fused model is the best in every column. C.5PROMPT-LENGTH TRUNCATION Short requests run 62 to 376 tokens, detailed descriptions 253 to 828, and StyleJSON specifications 1,102 to 3,731, so a 1,024-token window clips every StyleJSON prompt and nothing else. Token counts use the Qwen3 tokenizer, except for LongCat-Image, which is counted under its own tok- enization. Table 18 lists the baselines whose runs used a window shorter than the prompts, with the window taken from the run behind Table 1. LongCat-Image fixes a 512-token window and tok- enizes quoted spans word by word, so it also clips 40% of the short and 90% of the long prompts. Qwen-Image-2 is served through an API that does not publish its window; text placed after token 1,024 of a StyleJSON prompt almost never appears in its output, while text before that point does, so we treat 1,024 as its effective window. For each baseline we rescore InfoVis and the baseline on the prompts the baseline received in full. Every gap narrows. InfoVis stays ahead of the three open- source baselines on all three metrics and ties Qwen-Image-2 on text-to-slide generation. On the 80 prompts that no model truncates, InfoVis remains the best open-source model behind GPT-Image-2 and Nano-Banana-2. Truncation does not explain the StyleJSON pattern in Figure 3b: FLUX.2-dev, FLUX.2-klein-base-9B, BAGEL, and OmniGen2 receive every prompt in full and score 4.71, 3.43, 2.30, and 1.81 in StyleJSON Content Faithfulness, against 7.95 for InfoVis. Table 18: Baselines whose text window clips VisSlideBench prompts. Clipped counts are per cat- egory (115 short, 109 long, 93 StyleJSON). ∆ is InfoVis minus the baseline in CF/IF/AQ, on the prompts the baseline received in full and on the full T2I split of Table 1. BaselineWindowClipped (S/L/J)Full prompts∆ on full prompts∆ in Table 1 Qwen-Image1,0240 / 0 / 93224+0.56 / +0.96 / +1.22+1.92 / +1.67 / +1.85 Z-Image-Turbo1,0240 / 0 / 93224+0.56 / +0.46 / +0.89+1.76 / +1.17 / +1.23 LongCat-Image51246 / 98 / 9380+2.05 / +1.54 / +2.27+3.73 / +2.54 / +2.77 Qwen-Image-21,0240 / 0 / 93224 −0.05 / +0.35 /−0.30+1.24 / +1.12 / +0.64 DDOCUMENT-TO-SLIDE WORKFLOW The workflow uses InfoVis to render complete slide images from an input document. It connects document parsing, content planning, design planning, candidate selection, and optional PowerPoint export, as illustrated in Figure 7. Document parsing and content planning. A parser extracts the document’s text and visual assets, including figures and tables. A content planner (GPT-4.1 (OpenAI, 2025)) uses these materials to form an outline for the presentation and a content plan for each slide. Each plan specifies the text and visual assets to include on that page. The design planner then determines how to arrange and style this content. 37 Preprint 1 Parse & extract T Document Text Section 1 Section 2 Section 3 Visual assets Figures · tables 2 Plan slide content 1 Motivation 2 Method 3 Results Slide i · content Title + key points Selected visual assets 3 Add layout & style to content style_json Structured slide prompt Slide contentLayoutAppearance Text blocks Image references +Hierarchy Image placement Typography · colors Background · decor 4 Generate & select One prompt · K candidates per slide style_json Content + design Selected visual assets InfoVis Full-slide renderer Candidate 1 Why reward matters Reliable feedback for image editing AccuracyDiversityLearning Candidate 2 Why reward matters Reliable feedback for image editing AccuracyDiversityLearning Candidate K Why reward matters Reliable feedback for image editing AccuracyDiversityLearning External verifier Select highest score Why reward matters Reliable feedback for image editing AccuracyDiversityLearning Style reference Selected slide 1 Reliable feedback for image editing AccuracyDiversityLearning Selected slide 1 Cache selected slide 1 once From slide 2 Per-slide prompt + selected assets Optional PPTX export T OCR + layout Text, boxes, styles ✦ Remove text Inpaint background T Rebuild text boxes Keep figures as image assets P Editable text Generated visuals preserved Figure 7: Document-to-slide workflow with InfoVis. Content planning and design planning pro- duce structured per-slide prompts. InfoVis generates candidates that an external verifier ranks. The selected first slide serves as a style reference for subsequent slides. Optional PowerPoint export reconstructs editable text while retaining visual assets as images. Candidate slides are schematic illustrations. Structured design planning. The design planner (also GPT-4.1) converts each content plan into a style_json prompt that specifies text blocks, layout hierarchy, image placement, typography, colors, background, and decorative elements. InfoVis renders each prompt together with the selected visual assets. Candidate generation and selection. For each slide i, InfoVis generates K candidates from its design prompt z i and input imagesI i . An external verifier V (Qwen3.5-27B (Qwen Team, 2026a)) scores each candidate against the prompt, and the workflow keeps the highest-scoring candidate (Appendix C.1 gives the verifier configuration and K): y (k) i = G θ (z i ,I i ;ε k ), k ∗ i = argmax k∈1,...,K V (y (k) i ,z i ), y ∗ i = y (k ∗ i ) i .(3) Here, G θ is InfoVis and ε k is the sampling randomness. The first slide is generated without a style reference; its selected candidate y ∗ 1 then serves as the reference image for every later slide, which keeps the visual style consistent while each slide follows its own content plan. The SlidesGen-Bench run in Table 3 uses K = 2. PowerPoint export. The selected slide images can optionally be converted into PowerPoint files with editable text. OCR and layout analysis recover the text content, bounding boxes, and basic styles. The workflow removes the detected text and inpaints those regions to create a background image, then reconstructs the recognized text as native PowerPoint text boxes. Figures and tables are retained as cropped image assets at their detected positions, so charts and tables remain images rather than native, editable objects. ELIMITATIONS VisSlideBench-Edit is held out from the same construction pipeline as the editing training data, so it measures in-distribution editing quality; out-of-distribution evidence comes from BizGenEval for generation and from SlidesGen-Bench for the workflow. Aesthetic Quality remains the dimension with the largest gap to proprietary models, and knowledge- and reasoning-intensive slides, as in the BizGenEval hard split, lie outside what slide-specific supervision targets. Our generation SFT targets and the synthetic half of VisSlideBench-Gen are produced with a proprietary model, which ties both the training distribution and those evaluation cases to its style; Table 16 therefore reports generation results separately for real and synthetic cases. Finally, the data and benchmark cover English-language slides only. Appendix G illustrates failures in visual asset fidelity and the preser- vation of content outside an editing target. 38 Preprint FQUALITATIVE RESULTS Figures 8, 9, 10, and 11 show qualitative results across the four slide tasks. InfoVis generates coherent slide layouts from text, follows reference styles, faithfully integrates multiple visual assets, and performs both local and global edits while preserving unrelated content. Figure 12 further shows the document-to-slide workflow producing visually consistent multi-slide presentations with relevant textual and visual content. Figure 8: Showcases of InfoVis in text-to-slide image generation. 39 Preprint Figure 9: Showcases of InfoVis in template-conditioned slide image generation. 40 Preprint Figure 10: Showcases of InfoVis in multi-image grounded slide image generation. 41 Preprint Figure 11: Showcases of InfoVis in slide image editing. 42 Preprint Figure 12: Showcases of the document-to-slide workflow built on InfoVis. 43 Preprint GFAILURE CASES Figure 13 shows two failure cases of InfoVis, one in content fidelity and one in editing locality. Visual asset fidelity. In multi-image grounded generation, a supplied chart can be reconstructed rather than faithfully preserved. In panels (a)–(b), the generated slide retains the broad appearance of the chart but corrupts legend entries and axis labels and changes the bar-group structure. Editing locality and preservation. In panels (c)–(d), the instruction asks to replace only the two line graphs on the left with vertically stacked scatter plots. The output instead contains four plots and removes the table on the right, which lies outside the requested editing target. The model identifies what to change but does not reliably confine the change to the requested region. 1 Visual asset fidelity Multi-image grounded generation: the supplied chart is reconstructed with errors. (a) Input chart(b) Generated slide Five methods; frame counts 16, 64, and 256.Legend, axes, and bar groups are altered. 2 Editing locality and preservation Requested edit (abridged): replace only the two left-hand line graphs with scatter plots. (c) Original slide(d) Edited slide Right-hand table should remain unchanged.Table removed; four plots are generated. Figure 13: Failure cases of InfoVis. (a)–(b) A supplied chart and the generated slide show errors in visual asset fidelity; the solid outline marks the reconstructed chart. (c)–(d) An original slide and its edited output show changes beyond the requested left-hand graphs. Dashed outlines mark the table region that should have been preserved. The editing instruction above the panels is abridged. 44