Presenting a Paper is an Art: Self-Improvement Aesthetic Agents for Academic Presentations
Authors: Chengzhi Liu
Yuzhe YANG
Kaiwen Zhou
Zhen Zhang
Yue Fan
Yanan Xie
Peng Qi
Xin Eric Wang
Venue: ICLR 2026 Poster (2026)
Keywords: Presentation Generation
Self-improvement
AI for Academic Research
Human-Agent Interaction
Primary area: applications to robotics, autonomy, planning
TL;DR: We present EvoPresent, a self-improving multi-agent framework for generating high-quality academic presentations through narrative construction, aesthetic design, and virtual delivery.

Abstract: The promotion of academic papers has become an important means of enhancing research visibility. where the appeal of dissemination largely determines its effectiveness.
However, existing automated methods struggle limited storytelling, insufficient aesthetic quality, and constrained self-adjustment, making it difficult to achieve efficient and engaging dissemination. At the heart of those challenges is a simple principle: *there is no way to improve it when you cannot evaluate it right*.
To address this, we introduce **EvoPresent**, a self-improvement agent framework that unifies coherent narratives, aesthetic-aware designs, and realistic presentation delivery via virtual characters. 
Central to EvoPresent is **PresAesth**, a multi-task reinforcement learning (RL) aesthetic model that provides reliable aesthetic scoring, defect adjustment, and comparative feedback, enabling iterative self-improvement even under limited aesthetic training data. 
To systematically evaluate the methods, we introduce **EvoPresent Benchmark**, a comprehensive benchmark comprising: *Presentation Generation Quality*, built on 650 top-tier AI conference papers with multimodal resources (slides, videos  and scripts) to assess both content and design; and *Aesthetic Awareness*, consisting of 2,000 slide pairs with varying aesthetic levels, supporting joint training and evaluation on scoring, defect adjustment, and comparison. Our findings highlight that (i) High-quality feedback is essential for agent self-improvement, while initial capability alone does not guarantee effective self-correction.
(ii) Automated generation pipelines exhibit a trade-off between visual design and content construction. (iii) Multi-task RL training shows stronger generalization in aesthetic awareness tasks.

Published as a conference paper at ICLR 2026
Presenting a Paper is an Art: SELF-IMPROVEMENT AESTHETIC AGENTS FOR ACADEMIC PRESENTATIONS
Chengzhi Liu'; Yuzhe Yang', Kaiwen Kaiwen Zhou?, Zhen Zhang', Yue Fan", Yanan Xie Peng Qi Xin Eric Wang'
University of California, Santa Barbara University of California, Santa Cruz Uniphore
{chengzhi, yuzheyang, er ericxwang) @ucsb. edu
Project Page:
/
ABSTRACT
The promotion of academic papers has become an important means of enhancing research visibility. However, existing automated methods struggle limited story- telling, insufficient aesthetic quality, and constrained self-adjustment, making it difficult to achieve efficient and engaging dissemination. At the heart those challenges is simple principle: there is way to improve it when you cannot eval- uate it right. To address this, we introduce EvoPresent, a self self-improvement agent framework that unifies coherent narratives, aesthetic-aware designs, and realistic presentation delivery via virtual characters. Central to EvoPresent is PresAesth, multi-task reinforcement learning (RL) aesthetic model that provides reliable aesthetic scoring, defect adjustment, and comparative feedback, enabling iterative self-im seff-improvement even under limited aesthetic training data. To systematically evaluate the methods, we introduce EvoPresent Benchmark, comprehensive benchmark comprising: Presentation Generation Quality, built on 650 top-tier Al conference papers with multimodal resources (slides, videos and scripts) to assess both content and design; and Aesthetic Awareness, consisting of 2,000 slide pairs with varying aesthetic levels, supporting joint training and evaluation scoring, defect adjustment, and comparison. Our findings highlight that (i) High-quality feedback is essential for agent eelf-improvement, while initial capability alone does not guarantee effective self- coffecorrectonn. (ii) Automated generation pipelines exhibit trade-off between visual design and content construction. (iii) Multi-task RL training shows stronger generalization in aesthetic awareness tasks.
INTRODUCTION
As scholarly communication increasingly moves online, the promotion of academic papers has become a crucial means of enhancing research visibility. Among various formats, presentation videos stand out for their efficiency and intuitiveness. Despite recent progress in automatic generation, in- cluding paper-to-slide (Ge et al. 2025) Zheng et al. (025) generation and text-to-video synthesis (Shi al. 2025 Xue al. 2025), existing methods still present several notable limitations. Specifically, current approaches, as illustrated in Figure [ (b-c), suffer from limited narrative coherence due to di- rect extraction; restricted design flexibility from fixed templates; and the absence of seff-improvement mechanisms that leave systems overly dependent on manual intervention in academic communication. Second, presentation aesthetic evaluation remains underdeveloped, with existing methods lacking both adequate aesthetic awareness (Zhou et al., 2024b) and dedicated metrics, which limits the comprehensiveness and reliability evaluation. Moreover, current evaluation methods largely rely on VLM-as-judge methods (Hwang et al. 2025). further reducing their consistency and robustness.
To address the limitations of generation quality, we propose EvoPresent, as shown in Figure I[a), self-improving agent framework that follows traft-feedback-refnneneee iterative loop to produce academic presentations with both narrative coherence and aesthetic awareness. First, the Storyline
"Equal contribution

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Published as a conference paper at ICLR 2026
(A) Our Method (EvoPresent)
(B) PPTAgent & PresentAggnt
(C) Paper2Poster
Planner
Layout
Scholarige
Agent
Template Selaction
Fixed layout
Document
Document
Document
Slides/Scripte Script/Viddo Seneration
Aesthetic
MMddl
Slide or Video Generation
LLM Checker
Poster Generation
VLM Checker
Itenative
rexitio
Namrtive Quality
Visual Design
Narrative Qudlity
Visual Design Number of manudl revisions
Nerrative Quality
Visual Design
Format Diversity
Namber manuri reviions
Formet Diversity
x
Format Diversity
Number of manusl nevisiond
Figure I: Comparison between EvoPresent and other methods. (a) EvoPresent achieves high quality with
fewer itcration through its self- imfroveeeee framework, supporting multiple formats (vidcos, scripts, slides)
for more realistic presentation. (b) PPTAgent (Zheng al. (025) and PresentAgent (Shi et al. 2025)
lack content expressiveness and are limited by fixed templates. (c) Paper2Poster (Pang et al. 2025 lacks
flexibility and an effective visual checker, leading to poor visual design and requiring extensive adjustments.
Agent extracts core text and figures from papers to construct structured scripts. Next, the Scholar Agent expands the narrative and enriches content through tool selection (e.g., image generation), while the Design Agent handles layout design and visual rendering to produce slides and video frames. Finally, the Checker Agent evaluate the presentation's content and design while providing targeted feedback until it reaches the desired visual standard. In this process, the framework integrates PresAesth, multi-task reinforcement learning- based aesthetic model to support the agents' iterative self-improvement. Trained on limited aesthetic preference data, PresAesth leverages Group Relative Policy Optimization (GRPO) (Shao et al. 2024) for joint multi-task learning. consistently performs aesthetic scoring, defect correction, and pairwise comparison, ensuring reliable aesthetic perception and reasoning for the system. Through this framework, EvoPresent not only enhances narrative coherence and expressiveness but also achieves aesthetic-awan design, ultimately producing higher- quality and more engaging academic presentation.
To address the shortcomings of existing evaluation, we propose EvoPresent Benchmark (Sec. 4.. which integrates two components: (i) Presentation Generation Quality, covering 650 academic resources across multiple domains and formats (slides, videos and scripts) with human annotations. This combines both global and fine-grained evaluations: the former focuses on overall narrative coherence and aesthetics, quantified with objective metrics (e.g., perplexity), while the latter leverages VLM-as-Judge to assess content and design across eight dimensions. (ii) Aesthetic Awareness, a dataset with 2000 slide pairs constructed with controlled perturbations (e.g., layout change) to provide systematic framework for training and evaluation in aesthetic perception and reasoning.
We further conduct a systematic evaluation of EvoPresent against state-of of-tte--rtttt models and multi- agent approaches, yielding the following key findings: (i) Multi-task RI. demonstrates superior generalization in aesthetic awareness compared other training paradigms. (ii) High-quality feedback proves essential for the iterative celf-improvement of agent frameworks, yielding fewer iterations and faster progress. (iii) Agents' initial performance does not necessarily correlate with its correction ability, and high performance alone is insufficient to guarantee stronger correction. (iv) Automated generation tasks reveal an inherent trade-off between content construction and visual design, where aesthetic awareness emerges as the primary bottleneck. To sum up, our contributions are listed as follows:
We introduce EvoPresent, the first self-improvement multi-agent framework for generating realistic academic presentations with minimal human intervention.
We design PresAesth, a multi-task reinforcement learning aesthetic model that unifies scoring, defect adjustment, and comparison with limited human-preference aesthetic data.
We propose EvoPresent Benchmark, a comprehensive framework that supports systematic evalua- tion of generation task performance and joint iiining-evaluation of aesthetic awareness tasks.
Extensive experiments show that EvoPresent surpasses existing methods, delivering presentations of higher quality and engagement comparable to human-designed.
2 RELATED WORK
Automated Presentation Generation. Recent advances in multimodal large language models have driven progress in the automated generation of academic papers, including tasks such as academic
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(A) Presentution Generation Pipeline
Presentation Generation
Input
1. Content Extraction
2.Storyline Construction
Voice Generation
Decuments
Voice Person Image
-illi Personal Voice
Image Tables
Section Contents
Match Filter
Content Construction
Generation
B.Aesthetic Checker
4, Design and Render
3.Content Enhancement
Adjustments
Initial PresAesth
Elements Information
Initial Layout
Slide Rendering
L more wh space images
Tools Calling
2. Hose Figunt//gnashe close to their descristive text.
Image
Knowledge Search
Stagel: Layout
Stage2: Style
Training Pipeline
'Smmpee
(C) Iterative Self-Improvement
Aesthatic Aworeness Task
Poirwise Comparison
"Typography need odjustment...
Deficiency Adjustment
Aesthatic Scortng
Scoring: "4,2/10"
Defect Detection Give the Feeback
Human Labeling Venification
6,5/10"
Multi- Task GRPO
Verify
Reference Model
KL-Lods
Slide is Better
Typography "Loyout" 42/10", "8,5/10"
Model Response
Itenstive Improvement
Select the besit one
Policy Model
Figure 2: Overview of the EvoPresent framework. (a) EvoPresent first performs content extraction and voice generation, then constructs the storyline and script, followed by content enhancement using image generation and knowledge retrieval. Design and rendering are handled next, and the aesthetic checker evaluates the initial slide and provides adjustments. (b) PresAesth is trained on numannpreference aesthetic dataset via multiple tasks (scoring, defect adjustment, and comparison). (c) The PresAesth model guides the agent framework in iterative self-improvement.
slide creation (Zheng al. 2025 Ge et al. 2025 Shi al. 2025) and poster generation (Pang et al. 2025). However, the quality of the generated outputs often falls short of practical requirements. As shown in Figure[]]) PPTAgent and PresentAgent primarily rely direct content extraction and fixed templates, which results in limited narrative coherence. Figure [c) further indicates that while Paper2Poster employs VLM-based checker, its limited aesthetic and design perception constrains generation quality. Moreover, most existing methods lack a reliable self-im seff-improvement mechanism, leaving the generation process heavily dependent on manual intervention (Li et al. 2023). In contrast, EvoPresent integrates storyline construction with iterative self-refinement, ensuring high-quality academic presentations with minimal refinement.
Aesthetic Evaluation. The aesthetic evaluation of visual design remains formidable challenge, as existing VLMs still show a substantial gap in capturing the nuanced and subjective nature of aesthetics compared to human perception (Zhou et al., 2024b). Many methods are being explored,
such as developing systems for accurately scoring image designs (Hong et al. 2023 Yu al. 2021) and techniques for better aligning model outputs with human preferences (Zhou al. 2024a| Li ct al.
2025; Liao et al.) 2025p. However, existing methods are largely confined to natural image aesthetics and struggle with the higher subjectivity and complexity of academic scenarios (c.g.. slides), leading to unstable performance. In contrast, our proposed PresAesth, trained via multi-task RL, exhibits stronger aesthetic awareness, enabling more reliable evaluation of academic visual design.
EVOPRESENT FRAMEWORK
Overview. We propose EvoPresent. self- self-improvement agent framework built upon draft-feedhack-reviine cycle. The pipeline leverages four agents sequence: the Storyline Agent extracts key information from the paper to construct the storyline and script, the Scholar Agent enriches them with external knowledge and image generation tools, the Design Agent generates layouts and renders initial drafts of slides and video frames, and the Checker Agent evaluates content and design to provide targeted feedback for refinement. Especially, to overcome the challenge of aesthetic evaluation and ensure reliable adjustment,
integrate PresAesth (Sec. 3.2) multi-task RL aesthetic model that performs three core tasks mcorins, defect correction and pairwise comparison, thereby supporting the agent framework's effective iterative self-imprrvemennt
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3.1 EVOPRESENT WORKFLOW
Storyline Agent. Given an paper, the first step involves organizing the information and conceptual- izing the storyline. The Storyline agent first employs Marker (Paruchuri, 2025) to extract text and visual elements, storing them in unified library. To minimize redundancy and ensure completeness, the agent performs several key functions: (i) constructs a story framework by dividing the paper into thematic sections; (ii) reorganizes the text into coherent chapters, extracting key arguments and details; (iii) associates pertinent visuals and formulas with the content while filtering out irrelevant elements; (iv) generates a complete presentation script based on the constructed storyline.
Scholar Agent. The Scholar Agent optimizes the paper's storyline to enhance both the academic content and visual quality of the presentation. It first analyzes the storyline to identify gaps in academic information and enriches it through: (i) Knowledge Enrichment: using tools such as the ArXiv Metadata and Citation Parser (MCP) (Blazick, 2025) to access additional related knowledge. (ii) Visual Enhancement: employing GPT-4o (OpenAl, 2022) and (wen-Image (Wu et al. 2025 to generate relevant charts or diagrams, improving visual impact and aligning visuals with the text.
Design Agent. Once the storyline is defined, our Design Agent begins designing the presentation pages. This agent consists of two core components: the Layout Planner and the Style Render. The process begins with the Layout Planner, which addresses the crucial task of arranging elements with aesthetic precision. To overcome the instability of using an agent without visual feedback, the planner first generates an initial layout based text and image sizes to ensure element balance and correct aspect ratios. With a stable layout in place, the Style Render renders the overall visual design. It analyzes the overall theme to select suitable style from a predefined Cascading Style Sheets (CSS) library, applying only visual elements like colors, fonts, and backgrounds to ensure the layout remains flexible. Finally, the agent adjusts font sizes to create a clear information hierarchy and applies dynamic effects to enhance audience engagement. Both components directly impact the final quality, and our choice of HTML over the less stable PPTX format provides the necessary flexibility and aesthetic control for this workflow (see Appendix 12
for a format comparison).
Checker Agent. The Checker Agent a critical com- ponent designed for iterative refinement of presenta- tion. Integrated with the PresAesth model (Sec. [3.2]. it performs stable aesthetic perception of slides. As shown in Algorithm [ the agent evaluates the aes- thetic score of the slide in each iteration, terminating early if the score exceeds the threshold (The default aesthetic threshold is set to 8.0; further details are pro- vided in Appendix B.) and otherwise providing feed- back to the Layout Planner for refinement. The agent further compares scores across iterations, reverting
Algorithm Checker Agent Workflow
(Sec.3..2).
Require: Initial slides St0), Layout Planner L, Checker
Agent C. iterations I', threshold Se.
Ensure: Presentation video Vpne
1: Shea < S Scoresor <- 0 for to 0 1 1 do Score(t) + C.score(Stt)) D Scoorin
3:
if Score(t) > S.a then return Vprc + generate(S(t)) end if
6:
7:
U Ste)
8:
if >t>s 0 Score(!) < Score 1) then
9:
U Stt-1) Revert better version
to the degradation, and if no iteration meets the target score.
version in case of quality
10: end if
11: Feedback + C.feedback(U)
the version with the highest score selected as the
12:
50++) + L.ffnneeU, Feedback)
final output. The finalized slides are then paired with narration audio generated by a text-to-speech system,
13:
if Score > Scorew then
14:
Swir (( Score Score
15:
end if
with each audio segment temporally aligned to its relevant slide. The video presents slides for the du- ration of narration, optionally incorporating transitions. Through this iterative refinement process, the Checker Agent ensures both aesthetic consistency and stability. See Appendix F.T for video generation details.
16:
end for
17:
return Vyr geSerate(Ss...)
> Select the best
3.2 PRESAESTH: MULTI-TASK AESTHETIC AWARENESS MODEL
Tasks Formulation. Slides serve as the primary medium in academic presentations, and accurate perception and evaluation of their aesthetics is essential to overall quality. Accordingly, we define three core tasks for the PresAesth model: (i) Scoring: Given a single slide image as input, this task is to evaluate its absolute quality on numerical scale, providing a holistic quantitative assessment. (ii) Defect Adjustment: Taking a single slide image as input, this task requires identifying specific deficiencies and providing specific feedback for improvement. Deficiencies main categories: Composition & Layout, Typography, and Imagery & Visualizations. (iii) Comparison: Given a baseline slide image and two proposed revisions, the task is to identify which of the two
classified into three
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revisions offers superior improvement the baseline. Overall, these tasks jointly aim to endow the model with aesthetic awareness consistent with human preferences.
Multi-Tasks GRPO. To train PresAesth, employ GRPO, efficient RL method that leverages verified reward signals to capture the subjective nature aesthetic evaluation, with Qwen-2.5-V 2025) serving the base model. Unlike Supervised Fine-Tuning (SFT), which fails to learn complex aesthetics from simplistic ground-truth labels. RL approach allows the model to explore and develop sophisticated reasoning by learning from verified reward signals. Consequently, our model delivers both accurate aesthetic judgments and coherent reasoning, enabling precise and effective feedback to steer our agentic workflow. GRPO's group-based optimization intrinsically matches how humans make aesthetic judgments through comparative assessment rather than absolute scoring. We apply the same loss function as in (Shao et al., 2024), details are Appendix C.1
7B (Bai al.
To guide the training process, we design comprehensive reward function. The first component is a Format Reward, which ensures the model's outputs are structured and parsable. We require the model to articulate its reasoning process within (think) and (/think) tags, and present its final conclusion within (answer) and (/answer) tags, A reward of 1 is issued the response strictly adheres to this format, and 0 otherwise, encouraging the generation of well-organized outputs. The second component is an Accuracy Reward (Tace), which evaluates the correctness of the content within the answer tag. This reward is task task-dependent, formulated as:
II( c00omm Vccop))
for Comparison Task
[( Vaat) > a) for Adjustment Task
Tace
[[[[oscore - Ysscore < () for Scoring Task
where I(-) is the indicator function; Ocomp- Odef, and Oscore the model's outputs for each task, while Ncomp, Yders and Y/coree are the associated ground-truth labels. The function f(-) parses the model's textual feedback extract deficiency categories. and ( predefined tolerance thresholds for the F1 F1-score and scoring error, respectively. The overall reward for the i-th response, r((1), is the sum of
(8) its format and accuracy rewards: (f) Fimt + Facc- This reward structure provides distinct incentives for both correct formatting and factual accuracy. Training settings are detailed in Appendix C.2
4 EVOPRESENT BENCHMARK
4.1 TASK DEFINITION
The EvoPresent benchmark consists of two core tasks: (i) Presentation Generation Quality, which evaluates both content and design dimensions; and (ii) Aesthetic Awareness, established as an integrated training-evaluatio framework for aesthetic scoring, defect adjustment, and pairwise comparison. See Appendix for detailed benchmark settings.
Generation Quality. We evaluate the presentation generation quality using two dimensions: (i)
Global Evaluation, which employs objective metrics to assess overall performance. For content, measure coherence and fluency with Perplexity (PPL) and ROUGE-L (Lin) (004);; for design, we evaluate composition through Layout Balance and Aesthetic Scores derived from PresAesth (1-10 scale). (ii) across eight localized dimensions. The content dimensions include Fidelity, Clarity, Narrative and Engagement, while the visual design dimensions encompass Elements, Layout, Hierarchy and Color.
Evaluation leverages a VL.M-as-judge to assess presentations on a 1-5 scale
Aesthetic Awareness. To evaluate the model's aesthetic awareness, assess three tasks separately: scoring, defect adjustment, and comparison. Scoring is evaluated using the Mean Absolute Error (MAE) against human annotations. The defect adjsutment task uses the F1-score to measure categories in No Deficiency, Composition & Layout, Typography, and Imagery & Visualizations. The comparison task evaluated by Accuracy, where the model required to select the higher-quality slide.
4.2 BENCHMARK CONSTRUCTION
Data Collection. (i) Generation Quality evaluation, shown in Figure B(a), covers 650 papers (13,000 slides in total) from top Al conferences (c.g.. ICLR, NeurIPS), spanning multiple domains (c.g. CV, NLP). Each paper is accompanied by various formats: slides, videos, and scripts, all annotated
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(A)
(B)
Video dorntion
dide franse dine
Average siale seript token
97
Viáeo Duratisn (mis)
Serige Tikeens
50
159
50
200
60
Sumple Numbher
Sumpp
ACL
CVPR
Nanner of Slides (mii))
Aserage Stdee Frome Time (1)
Dufirkncy
Typegraphy
ICEA
Composttion & Layost
Vaniltattion
43%
39
Sample Count
Sumple Count
50
Tansl
Tixess ranger 3625-2035
14.5
EEGG Sackey
lnteticc placry & Aggicaaiion Therry
Paradgro
Score
Slidas Deficieny Calegurias
Figure 3: Data Statistics for our Benchmark. (a) The categories of papers across different vemues. (b) Distribution of presentation videos and scripts, including slide counts, video duration, average slide frame time, and slide script tokens. (c) The overall scores and deficiency categorics of aesthetic awareness data.
by 2-3 experts. As shown Table T existing automated generation benchmarks typically have fewer samples and are limited in domain and formats, whereas EvoPresent provides comprehensive evaluation across multiple modalities. (ii) The Aesthetic awareness suite is constructed through a multi-stage human annotation process. We apply perturbations (e.g., style changes) the original slides from the generation quality evaluation, producing three variants of different visual quality (labeled as poor, base and good) and forming slide pairs accordingly. Each slide is independently annotated by 2-3 annotators with both quality ratings and defect labels, ensuring applicability to aesthetic scoring, defect detection, and comparison tasks. The dataset comprises 2,000 slide pairs (6,000 slides in total), with 1,600 used for training and 400 for testing. Details of data collection are in Appendix B.2
Data Statistics. (i) As shown in Figure 3)), the Gener- ation Quality evaluation task spans various presentation formats to ensure diversity. Source papers average 9 pages of technical content, while corresponding presentation videos average 9.5 minutes. The number of slides ranges from 6-19, with display times of 10-100 seconds per slide, and script lengths from 50-30 300 words, reflecting diverse presentation styles. (ii) In the Aesthetic Awareness task, illustrated in Figure (), the aesthetic score distribution
Method
Domain Sample Video Script
PPTAgent Paper2Poster
500
100
P2P
121
PresentAgent
30
EvoPresent (ours)
650
Table 1: Comparison of different bench- marks for generation and evaluation.
is relatively balanced, with most concentrated between 5-7. Each slide contains at least 2-3 design errors across different categories ensure the diversity and appropriate difficulty of the evaluation.
EXPERIMENTS
Baseline, For the Generation Quality evaluation, we compare four categories of methods: Oracle method, where the original slides serve as the upper bound fo visual quality and the script as the gold standard for content; (ii) End-to-end methods, including HTML--based generation (GPT-4o, GPT-5 (OpenAi 2025a). Claude-4-Sonnet (Anthropic) 2025), DeepSeek-R1 (DeepSeek-Al et al. 2025)) and Image-based generation GPT-4o-Image; (iii) Multi-agent methods, including PPTA- gent, PresentAgent, and Paper2poster; (iv) Our method, EvoPresent. For the Aesthetic Awareness evaluation, we assess the same set of closed-source models as introduced above, together with the open-source models GLM-4.5V (Team al., 2025). Owen-VL-7B/322 (Bai al. 2025 and VLAA-Thinker-77 (Chen et all 2025 Details methods and settings are provided in Appendix D.I
5.1 EVALUATION AND ANALYSIS
Presentation Quality Results. As shown in Table EvoPresent outperforms existing methods in both content and visual design. While end-to-end models like GPT-4o maintain content integrity, they lack aesthetic perception, resulting in weaker visual appeal. In contrast, EvoPresent-4o reduces perplexity by about 17% and achieves notable improvements in fine-grained metrics. Although

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Published as a conference paper at ICLR 2026
Global Evaluation
Fine-Grained Evaluation
PPL
ROUGE-L
Balance
Aesth.
Content scoret
Design score"
Overall
Fid. Clar. Nar.
Eng.
Ele.
Lay.
Hier.
Color.
Oracle methods
Slides + Scripts
16.64
20.53
0.82
8.50
4,32
4,18
4.13
3.95
3,90
3.78
4,00
3.78
4.01
End-to-end methods
GPT-4o
24.32
12.59
0.70
7.05
3.83
3.48
3.63
3,57
3.34
3.44
3,76
3.65
3.58
GPT-4o-Image
56.50
7.69
0.67
6.84
3.67
3.21
3.15
3.50
3.24
3.35
3.57
3.30
3.37
GPT:5
24.48
12.72
0.72
7.80
4,02
3,56
3.96
3.73
3,70
3.49
3.92
3,68
3.76
Deepseck-RI
25.58
10.40
0.64
7.52
3,93
3.45
3.82
3.56
3
3.25
3,80
3.64
3.61
Claude 4-Sonnet
22.02
14.87
0.72
7.70
4.03
3.56
3.98
3.86
3.60
3.52
3.96
3.69
3,78
Multi Aeeltt--Agettaeee methods
PPTAgent-Fo
23.45
12.04
0.73
7.28
3.90
3.35
3.66
3.57
3.52
3.50
3.77
3.00
3.03
PresentAgent-4o
22.80
12.69
7.42
3,92
3.97
3.80
3.61
3.52
3.79
3.66
3.75
Paper2poster-4o
22.23
13.64
0.71
7.65
3,93
3.72
3.95
3.84
3.63
3.50
3.72
3.75
3,76
EvoPresent (ours)
EvoPresent-4o
20.00
14.68
0.67
7.82
3.95
3.74
4.08
3.85
3.63
3.63
3.78
3,80
3.82
EvoPresent-gnu3 21.35
15.85
8.15
4.06
3.69
3.77
3.87
EvoPresent-rl
13.39
0.73
7.74
3,98
3.80
4.06
3.83
3.66
3.49
3.99
3.67
3.81
EvoPresent-clane claude-4
18.57
16.78
0.78
8.05
4.05
3.94
4.09
3.92
3.67
3.65
4.03
3.86
3,90
Table 2: Quantitative results of presentation quality. The evaluation covers both content and design aspects global and fine-grained levels. Global metries include Perplexity, ROUGE-L, Layout Balance and Aesthetic scores (1-10 scale). Fine-grained evaluation divides into content dimensions (Fidelity, Clarity, Narrative, Engagement) and design (Elements, Layout, Hierarchy. Color), all rated on 1-5 scale.
reasoning models like Deepseek-RI and GPT-5 produce better visual quality, they tend to introduce redundancies, leading to higher perplexity. This reveals trade-off between aesthetics design and content construction. Compared to multi-agent methods, EvoPresent stands out in narrative and engagement, achieving superior visual quality due to its iterative seff-improvemmnt process. Moreover, models with stronger capabilities, such as Claude 4-sonnet, perform better in aesthetics, particularly in HTML rendering, where they refine visual elements with greater precision and effectiveness. Notably, the differences in content quality across most models are relatively small, with their main distinctions more evident in aesthetics and design capabilities.
Agrnt
Aesthetic Awareness Results. To effectively evaluate the aligmment of models with human preferences, we compare PresAesth with other models across three aes- thetic awareness tasks, As shown in Table PresAesth achieves consistently superior performance across all three aesthetic tasks. For scoring, its MAE is on average about 18% lower than that of closed-source models such as GPT-4o and Claude-4-sonnet, indicating predictions more closely aligned with human judgments. In terms of defect adjustment, although some models perform well individual dimensions, Pres achieves ble performance across all aspects and attains the best overall score, reflecting more reliable detection capabili- ties. Most notably, in the comparison task, it achieves an accuracy of 87, 77, on average about 40% higher than other models, and further provides structured
Clonde-4 sonnet
GPT-4--HMIL
DeeparekoRi
Narcativve
Temperd Voe
Oppn Euerve
(b)
Figure 4: Evaluation of the presentation ex- perience. (a) Video performance assessed on
4 dimensions. (b) Content delivery evaluated with verbatim and explanatory questions.
Adjustment (FI-Score 1)
Scoring (MAE 4)
Comparison (Acc. f]
Method
Layout & Composition
Imagery Visualizations
Typography
Avg.
VLAA-Thinee--7B
1.92
0.529
0.417
0.156
0.367
0.565
Qwen2.5.
1.45
0.521
0.432
0.173
0.375
0,542
Qwen2.5VI-32B
1.90
0.533
0.455
0.170
0.386
0.615
GL.M-4.5V
1.99
0.525
0.441
0.162
0.376
0.662
Claude-4-sonnet
1.61
0.532
0.449
0.178
0.386
0.695
GPT-4o
1.64
0.534
0,451
0.172
0,386
0.771
GPT-5
1.39
0.534
0.452
0.171
0,386
0.597
PresAesth (Ours)
1.33
0.535
0.443
0.189
0.389
0.878
Table 3: Quantitative results of three aesthetic tasks. All results evaluated on and averaged over the aesthetic awareness test set of 400 sample pairs (Sec. 4.2). Additional results are in Appendix D.2
D.2
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(A) Human Presentation
(B) EvoPresent-fo
Gytaniiee Cenento Seommating
(C) PresentAgent-fo
Poper2Poster-fo
GPT-S-HTML (F) GPT-to-Image
Figure 5: Illustration of presentation variants by different methods: (a) Author-designed, (b) Our EvoPresent, (c) PresentAgent, (d) Paper2poster, (c) GPTS-HTML (web-based), (f) GPT-4o-Image (pixel- based). The figure highlights several common design deficiencies marked with colored boxes: (1) overlap issues, (2) content errors, (3) typography defects, and (4) unbalanced layout design. The results indicate that existing generation methods generally exhibit deficiencies in aesthetic design, whereas our method achieves the closest visual alignment with the human-designed reference. Additional visualization and results are presented in Appendix E.2
reasoning that enhances the explainability of its feedback. These results indicate that multi-tass RL training achieves stronger generalization and further enhances performance in aesthetic awareness..
Presentation Experience Evaluation. To evaluate the overall experience of the presentation, we analyze both video performance and the delivery of core paper content. Video performance is assessed by Qwen-Omnic four dimensions: comprehension difficulty, narrative coherence, temporal fluency, and conciseness (1-5 scale). As shown in Figure 4 [). EvoPresent consistently achieves the best scores. For content delivery, randomly sample 30 papers from PaperQuiz (Pang et al.) 2025) and use both an open- source model Qwen2.5-VL- VV-32BB and a closed-sourg model GPT-4o to simulate different reading levels. These models read the slides and scripts and answer verbatim (directly answerable from paper) and explanatory questions (higher-level comprehension) As shown in Figure 4b) our method attains higher accuracy in both questions, effectively conveying paper core knowledge.
(Xu et al. 2025), which simulates human viewing and rates videos on
Qualitative Comparison. We present a quanti- tative comparison of different methods for two oral paper (Zha et al. 2025 Qi et al., 2024).
Preferred the former (%) Preferred the latter
(A) Slides Poster
Presentation Video
As shown in Figure 5 existing methods exhibit notable limitations in both aesthetic design and content organization. Specifically, template- driven methods such as PresentAgent often sult in rigid layouts with frequent boundary er- rors and suboptimal whitespace allocation. The method GPT-4o-Image generates visually plau- sible outputs, while the textual content often blurred and illegible. Furthermore, structure- based methods such as GPT5-HTML provide stability but remain overly text-heavy, lacking visual hierarchy. contrast, EvoPresent ad- dresses these issues by establishing clear hierarchy and a coherent multi-page narrative flow.
Percentuge (%)
OervPPY Ageut
OurdPreeentgen
SorrPaper1Potte OurvOrigiosl
GorsPreseutAgm Agent
Orighte)
AgesentAgent Original
Figure 6: Human evaluation of the generation based on the EvoPresent benchmark, with results reflecting the average preferences of all evaluators.
Human evaluation. To further evaluate EvoPresent, we conduct pairwise human preference evalua- tions with five volunteers, comparing EyoPresent-claude- with other methods and human-generated slides and videos. As shown in Figure 6 Preferred the former (%) and Preferred the latter (%) respec- tively indicate the proportion of participants favoring the first second option in each comparison. Detailed criteria are in Appendix G The results show that EvoPresent outperforms other methods in generation quality and is competitive with human-generated presentations.
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5.2 ABLATION STUDY
Impact of Core Agents. We conduct ablation exper- iments in the EvoPresent-4o setup by removing the Scholar, Design, and Checker Agents. Results in Ta- ble 4 show that the Scholar Agent enhances content coverage and narrative depth, with its removal reducing the content score from 3.91 to 3.40. The Design Agent ensures layout balance and readability, and excluding decreases the design by 10.2%. The Checker pre- serves detail alignment and visual consistency, without it, design drops by 5.1 % and aesthetics by 15%.
Scholar Design Checker Content
Design 1 Aesth
3.40
3.73
7.53
3.91
3.35
7,03
3.91
3,54
6.40
3.73
Table 4:
Ablation study of the core agents.
Content and Design (1-5 scales) are averaged over their four metrics, and Aesthetics (1-10 scule) is obtained from the PresAesth All metrics defined in Section 4.1
Multi- -ask Generalizabiliity We compare several train- as baseline, with results in Table 5 Fine-tuning with GRPO on individual tasks results in performance drops relative to PresAesth, suggesting that aesthetic awareness tasky are not iso- lated but exhibit inherent dependencies. This may be due to their adherence to shared aesthetic principles, such as balance and visual consistency. We further compare Pre- sAesth with a multi-task model trained via supervised fine-tuning (SFT). While multi-tass SFT achieves higher accuracy than single-task RL training, it only outputs static labels without actionable feedback. In contrast, GRPO-based training drives the model actively perceive aesthetics and generate targed feedback, better aligning with the reff-impovving agent paradigm.
ing strategies using @wwn2.55VVL.
Scoring (MAF.
Adjustment (F1-Score 1)
Comparison
Method
(Acc.
Scoring Only
1.42
0.370
0.550
Adjustment Only
1.90
Comparison Only
1.79
0.373
0.719
Multi-Task SFT
1.73
0.334
0.872
Multi-Tasks GRPO
1.33
0.389
0.878
Table Ablation study on different training strategies under multi-task paradigm.
Effect of Self-Improvement. To further evaluate the role of aesthetic model PresAesth in iterative self- improvement, we integrate different models into the checker module and assess EvoPresent-4o's improvements in presentation aesthetic score. As
Aesthetic Score
Avg. Improvement
(B)
Derrationn
Dirraiinn
Preskeoth (Our)
Bteration
Tieration
Qwent S-LL-21
Mavuuae surdur ad eanan
Nestaa Store
GPT-40
Dispnd-a
80.0%
1415%
shown in Figure Ja), a), when PresAesth serves as the checker, the agent im- proves from 3.2 to three iterations, whereas other check- ers require more than five iterations and achieve lower final scores. This indicates that high-quality feedback can enhance the agent's 's capacity for iterative refinement and speed up the improvement process. We further evaluate the self self-correction ability of different models the aesthetic defect adjustment task with PresAesth the checker, illustrated in Figure bb). The results indicate that although GPT-4o and DeepSeek-R achieve adequate initial performance, their gains within 1-2 iterations remain below 20%. Similarly, more capable models such Gemini-2.5-p and Claude-4-Sonie achieve higher initial accuracy but still demonstrate limited self-correction ability. This indicates that an agent's 's initial performance does not necessarily correlate with correction, and high performance alone is insufficient to guarantee stronger self refinement.
8.0 within
Tnitial
Number Self-Imprroemmme
3rd
4tb
5th
6th
25.0%
Figure 7: Analysis of agent self-improvemmntt (A) Aesthetic score over ilerations with various models. (B) Distribution of iteration self-correction the aesthetic defect adjustment.
CONCLUSION
We introduce EvoPresent, a seff-improvement framework for academic presentation generation. The framework is built upon the EvoPresent benchmark, which provides systematic setting for evaluating presentation generation quality and supports integrated training-evaluation of multi-task aesthetic awareness tasks. By training the PresAesth model with multi-task GRPO, we address key limitations of existing methods, such as limited aesthetic awareness. EvoPresent leverages iterative westhetic-aware optimization to generate high-quality presentations aligned with human preferences, enabling more engaging dissemination of research and knowledge.

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ETHICS STATEMENT
The data utilized in this research were exclusively collected from publicty accessible websites of academic conferences. Our data collection process confined information made publicly available by the event organizers for informational purposes. No private or sensitive user data was gathered analyzed in this study.
REPRODUCIBILITY STATEMENT
The code and some examples of this project available in the supplementary material.
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Content of Appendix
A Use of LLMs
14
EvoPresent Bechmark
15
B.1 Metries
B.2 Data Construction
Multi-task aesthetic awareness RL training
C.1 GRPO Details
C.2 Training Settings
D Experiments
18
D.1 Baselines
18
D.2 More Results
18
D.3 Ablation Study
19
D.4 Different Format
Case Study
E.1
PresAesth Example Outputs
Presentation Generation
E.3
More annotation examples.
Iterative Seff-Improvement Process
F.1
Presentation Video Generation
36
G Human Evaluation
36
G.1 Checklist
36
H Prompts Design
H.1 Training Prompts
3
H.2 Generation Quality Evaluation Prompts
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A USE OF LLMS
The use of LLMs in this study was strictly limited to their function writing and editing assistants. Their role involved improving the manuscript's grammar, spelling, and stylistic consistency, LLMs did not contribute to any core scientific aspects of the work, such as research ideation, experimental design, data analysis, or interpretation of results. All substantive content and intellectual contributions solely the work of the authors.
B AESTHETIC THRESHOLD SELECTION
To enable stable iterative refinement, the Checker Agent adopts an aesthetic threshold to determine convergence of the revision loop. We empirically set this threshold 8.0. To justify this choice, evaluate multiple candidate thresholds using both aesthetic quality and computational efficiency. As summarized Tlole thresholds lead fewer refinement iterations and reduced inference time but noticeably degrade the final aesthetic quality of generated slides. In contrast, higher thresholds significantly increase the number of iterations and overall inference cost, while yielding only marginal improvements in aesthetic scores. A threshold of 8.0 thus provides a balanced trade-off between refinement quality and computational overhead, ensuring efficient yet high-quality self-improvement.
Beyond threshold selection, we further study whether a single global threshold is suitable for slides with different content structures. The EvoPresent Benchmark includes broad range of academic slide styles, including text-only, image-only, text-dominant mixed and image-dominant mixed slides. PresAesth is therefore trained to produce a unified aesthetic judgment that is not tied to lpyout- patterns. To validate this design. group slides into the above three categories and compute the correlation and error between model predictions and human annotations within each group. As shown in Table 7 the four categories exhibit highly comparable correlation coefficients and error levels, demonstrating that PresAesth maintains stable aesthetic perception across varying slide types.
Threshold Sth.
Final Aesthetic Score
Avg. Iterations
6.5
7.36
4.80
7.0
7.42
5.00
7.5
7.80
5.80
8.0
8.05
6.20
8.5
8.09
8.90
9.0
8.10
10.50
Table 6: Analysis of aesthetic thresholds. Lower thresholds reduce iteration cost but degrade the final aesthetic quality, while higher thresholds increase computational overhead with marginal gains.
Slide Type
Pearson r
MAE 1
Text-only slides
0.858
1.325
Image-only slides
0.854
1.330
Text-dominant mixed slides
0.847
1.316
Image-dominant visual slides
0.853
1.310
Table 7: Aesthetic prediction consistency across different slide types.
C MORE ANNOTATION DETAILS
Annotation Consistency. To ensure the reliability of the subjective annotations used in EvoPresent Benchmark, we evaluate annotator consistency across both continuous rating tasks and discrete classification tasks. Appendix G.] provides the interfaces of our annotation platform and detailed task checklists, while Appendix|E.1 E. presents representative annotated examples illustrating the workflow and decision criteria.
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Task
Metric
Consistency Score
Aesthetic Score (1-10)
ICC
0.92
Presentation Quality Score (0-5)
ICC
0.95
Defect Detection
Fleiss' Kappa
0.80
Pairwise Aesthetic Comparison
Fleiss' Kappa
0.83
Table 8: Inter-annotator consistency across different annotation tasks.
For continuous aesthetic and cicenntationqqualii ratings, we compute the Inter-Class Correlation (ICC) (Shrout & Fleiss 1979) assess agreement among multiple annotators. The ICC is defined as:
MSp MSW
ICC
(1)
MSp + (k- 1) & (MSR MSw)
where MS denotes the between-target mean square, SMSw the within-targe mean square, S the between-rater mean square, l the number of annotators, and n the number of items. Higher ICC values indicate stronger consistency in continuous numerical ratings.
For discrete annotation tasks such as defect detection and pairwise comparison, we compute Fleiss' Kappa (Fleiss) 1971):
P- Pe
1 1- Pe
where P denotes the observed agreement among annotators and P. represents the expected agreement by chance. A value of 0.6 is generally considered indicate substantial agreement.
As summarized Table 6, annotators exhibit consistently high agreement across all tasks, reflected by strong ICC values, high Fleiss' Kappa scores, and low variance. These results demonstrate that the benchmark provides stable and reliable subjective annotations, forming robust foundation for training and evaluating the PresAesth model.
We have presented extensive case studies and sample data in Appendix To more comprehensively and clearly illustrate the composition of our benchmark and its annotation workflow, we further provide additional representative annotation examples in Appendix E.3
More Data example.
B EVoPRESENT BECHMARK
B. METRICS
In this section, detail the metrics used in benchmark, where referenced several visual design and perception theory literature (Wertheimer) (201)), and selected several key evaluation indicators. These metrics are primarily grounded in Gestalt perceptual principles (Gordon! 2020 and visual hierarchy theory to quantify various types of design flaws in presentation slides.
Perplexity (PPL). We evaluate narrative coherence by computing perplexity using Qwen2.5-VL- 7B. For each slide, we input both the current slide image and the preceding slide image to assess contextual fluency. The perplexity is calculated as:
1
PPL exp
Σ 1=1
log P(wil Teums Aprevs W1, .... wi-1)
where P(ws/Aeure Aprevs W1, W;-1) is the conditional probability of token wy given the current slide image Teurs previous slide image Tprev and preceding text context. Lower perplexity indicates better narrative flow and contextual coherence across slides.
ROUGE-L. ROUGE-L evaluates content fidelity by measuring the longest common subsequence (LCS) between generated presentation scripts and reference content from the original paper. It is computed as:
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ROUGE-L (1+ (2) RRiss Pies
R + Mes
where:
LCS(X,Y) [
Mis LCS(X,Y)
Rics
|X|
Here, X the generated script, is the reference content, LCS(X, Y) denotes the length of the longest common subsequence, and 8 is typically set to for balanced precision and recall. Higher ROUGE-L scores indicate better content preservation from the source material.
Layout Balance. We first segment the slide image to identify individual visual elements and their bounding boxes, then compute the area-weighted center of mass to assess visual balance. The balance score is calculated as:
Balance max
0,
dmax
where d the distance from the center mass to the slide center, and dmax possible distance. The center of mass (Zcoms fcom) is computed as:
v2
2 is the maximum
Σ 11=1 A Fe,s
Ei= A Vc..
cpom
D Ai
Yeou
E A
where A; is the area of element and (Fe, (e,i) is its center coordinate. Higher balance scores indicate better visual equilibrium.
Fidelity. This metric measures how accurately and faithfully the content the slide represents the source information or the intended message. It assesses whether the content is free from factual errors, misrepresentationss, or discrepancies with the source data. In short, it evaluates the truthfulness and accuracy of the content.
Clarity.
This metric evaluates how easy it is to understand the text, charts, and data presented on the slide. It considers whether the language is concise, unambiguous, and free of jargon, allowing the audience to grasp the key points effortlessly.
This metric assesses whether the content on the slides tells coherent and compelling story. It evaluates if there is logical flow that guides the audience from one point to the next, forming complete and persuasive argument or story arc.
Narrative.
Engagement. This metric measures the content's ability to capture and hold the audience's e's attention. It assesses whether the material is interesting, wroghh-provoiigg.. or persuasive, making the audience curious and invested in the topic.
Elements. This refers to the quality and appropriateness of the individual visual components used on the slide, such as fonts (typography), images, icons, charts, and shapes. evaluates whether these elements are high-quality, relevant, and used effectively.
Layout. This metric evaluates the arrangement and spatial organization of elements on the slide. It assesses whether the composition is balanced, uncluttered, and makes effective use of whitespace. good layout guides the viewer's eye in a logical and visually pleasing path.
Hierarehy. This metric assesses whether there is clear visual distinction between primary and secondary information. It evaluates the audience can immediately identify titles, main points, and supporting details. This is typically achieved through variations in font size, weight (boldness), color, and placement.
Color. This metric evaluates the use of the color scheme the slide. It considers whether the palette aesthetically pleasing and professional, the contrast sufficient for readability, and if color is used purposefully highlight key information, categorize data, create specific mood.
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B.2 DATA CONSTRUCTION
Our data sources include real slide data as well as diversified variant images generated from reference slides through data augmentation strategies. These variants may contain design improvements or design flaws. We employ three predefined types of modifications to create slide variants of different quality levels: (i) within-object alignment alterations that modify the internal structure of individual elements, such as adjusting text alignment or subcomponent positions; (ii) between-object layout alterations that adjust spatial relationships between entire elements, including scaling, repositioning. and spacing adjustments; (ii) typography alterations that change font size, weight, and spacing properties.
By applying these modification strategies to reference slides, we generate image pairs with different design qualities, providing diverse test samples for evaluating design improvement and defect identification algorithms. This approach ensures that the generated variants include both positive modifications that may enhance visual effects and negative changes that may reduce design quality. All generated image variants are ultimately assessed and annotated by human annotators to establish reliable evaluation benchmarks.
C MULTI-TASK AESTHETIC AWARENESS RL TRAINING
C.I GRPO DETAILS
We use the Group-wiss wise Reward Policy Optimization (GRPO) algorithm for the fine-tuning phase. For each query q, GRPO samples N = 8 responses (o(2), o(2) ..... ,o(") from an old policy Thw and
obtains corresponding rewards {r(1), ,,(), ......))))))).......)..)).)))))) ((N) from the reward model. To enable differential
reinforcement based on relative quality, GRPO computes the normalized advantage for each response:
(() mean(( (r(1), , ...,,,(()))
A
std((r(1), ,r(), ,,2)....((N))) r(N)))
where A0) represents the normalized quality of the i-th response relative to others in the same group. This normalization ensures the model learns from comparative quality differences rather than absolute values. The optimization objective is defined as:
[(]) Fly-Q.o { min (p() A (p(9, - 1,,,1-1,11 -, ]0
B lx. [= [
where the policy ratio (()) TT (o(*) ()) / Tea (o |q) measures the change in probability of generating a response, and the KL divergence term regularizes the policy to maintain training stability by preventing large deviations from reference model.
C.2 TRAINING SETTINGS
In this section, we detail the configuration of the key hyperparameters and data preprocessing steps for our three core tasks. Hyperparameter values were determined through empirical analysis held-out validation set to ensure that the reward signals were both meaningful and sufficiently dense facilitate effective learning. We initialize our AesthEval model from the Qwen-VL- VL-7B checkpoint. The entire training process was conducted on 8 NVIDIA H100 GPUs The model was trained for total of 2 epochs. During the GRPO optimization phase, for each query, we sample N responses in each rollout to compute the relative advantages and update the policy.
Training Dataset. Our training dataset composite of two distinct, Luman-annotated sources, designed to cover both high-quality academic and general-domain slides. The first portion is academic in nature, derived from the conference presentations we collected for our EvoPresent benchmark. To incorporate examples with identifiable deficiencies, the second portion is sourced from the pre- annotated SlideAudit (Zhang et al., (2255) dataset, which contains slides from the general domain. In total, our training data comprises approximately 3,400 instances, broken down by task: 500 for Pairwise Comparison, 1,900 for defect detection, and 1,000 for Scoring.
Image Preprocessing. Since our dataset is aggregated from various sources, input images are of inconsistent dimensions. To standardize the inputs, we normalize the image sizes. For the single- image tasks (Adjustment and Scoring), we resize each image that its longest edge is 960 pixels,
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while maintaining its original aspect ratio. For the Comparison task, which processes three images simultaneously. we use smaller resolution to manage the increased computational load; the longest edge of each of the three images is resized to 720 pixels.
Adjustment Task Threshold (a), For the Adjustment Task, the reward is based on the Fl-score, which measures the overlap between the model-identified deficiency categories and the ground-truth labels. We set the Fl-score threshold to a = 0.5. This value was chosen as it strikes a balance: is high enough ensure that the model's feedback is substantively correct, yet flexible enough to overly penalize minor or stylistic omissions in the identified deficiencies. Preliminary experiments showed that higher threshold resulted in a sparse reward signal that hindered stable training.
Scoring Task Threshold ((). The Scoring Task requires the model to output numerical quality score, which, in our benchmark, on scale from to 10. For this task, we set the absolute error tolerance threshold to Ç = 0.25. This means a predicted score is deemed correct if it is within +0.5 of the ground-truth score (e.g.. if the true score is 8.5, any prediction from 8.25 8.75 receives reward). This tolerance accounts for the inherent subjectivity in aesthetic judgment and provides stable learning signal by rewarding outputs that are "close enough" to the human consensus.
KL Regularization Coefficient (3). In the GRPO objective function, the hyperparameter 3 controls the weight of the KI. divergence penalty, which regularizes the policy update to prevent it from deviating too drastically from the reference policy. A well-tuned 3 is crucial for stable training. Based on our validation experiments, we set B = 0.001. This small value provides sufficient regularization to stabilize learning without overly constraining the policy optimization.
D EXPERIMENTS
D.1 BASELINES
PPTAgent. PPTAgent employs two-stage methodology: first analyzing reference presentations to cluster slides into functional types and extract content schemas, then generating new presentations by creating an outline that maps document sections to reference slides and producing editing actions to modify them. The system uses HTML rendering for easier manipulation and includes self-correction mechanisms to handle execution errors.
Poser2Poster. PosterAgent uses a three-stage approach: the Parser extracts paper content into structured text and visual asset libraries, the Planner semantically matches assets and generates binary-tree layouts preserving reading order, and the panels where the Painter generates bullet points and executable code while VLM Commenter provides visual feedback to prevent overflow and ensure alignment. This top-down, visual-in-the-loop design compresses 20K+ token papers into coherent single-page posters.
loop iteratively refines
PresentAgent, PresentAgent uses a four-stage pipeline: document segmentation into semantic blocks with assigned slide types, slide composition through editable operations on HTML layouts rendered via python-pptx. narration generation using LLM prompting followed by text-to-speech synthesis, and video assembly that combines slide images with time-aligned audio into synchronized presentation videos.
GPT-4o-Image, This baseline leverages GPT-4o's integrated vision generation capabilities through the web interface. The method takes the input document and a structured prompt as input, then directly generates poster images using GPT-4o's multimodal generation pipeline. The generated images are exported in standard formats for evaluation.
bbaed end-to-end methods. These approaches use GPT-5 to convert input documents into structured HTML markup, which is then rendered into visual presentations. The pipeline involves prompting GPT-5 with the document content to generate HTML code containing layout structures, styling, and content organization. The resulting HTML is subsequently rendered using web browsers or HTML-to-image conversion tools to produce the final presentation images.
D.2 MORE RESULTS
Presentation Generation Quality.
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Global Evaluation
Fine-Grained Evaluation
Model
PPL.
ROUGE-L Balance 1
Aesth. 1
Content score!
Design scoref
Overall
Fid. Clar. Nar.
Eng.
Ele.
Hier.
Color.
Base methods
Slides + Seripts
16.64
20.53
0.82
8.50
4.32
4.18
3,95
3.00
4,00
3.78
4.01
Mulri-Agent methods
PPTAgent-qwen
21.03
13.41
0.74
7.69
3.08
3.62
3.65
3.61
3,56
3.00
3,78
3.62
3.68
Paper2poste-qwen
21.23
14.05
0.72
7.73
3.99
3.76
3.98
3.86
3.66
3.54
3.73
3.75
3.78
PresentAgent
22.15
12.99
0.69
7.50
3.97
3.76
4.02
3.85
3.67
3.56
3.80
3.66
3.79
Present PresentAgent- gemini-2.5-11 p.r
20.01
13.45
0.73
7.56
4.01
3.79
4.04
3.87
3,68
3.58
3.82
3.68
3.81
PessemtAgentcllud-3777445 3.7-omett
20.05
13.50
0.74
7.55
3.98
3.79
3.88
3.65
3.59
3.83
3.69
3.81
EvoPresent (our))
EvaPresent
ExoPreseet--peee255 qwen2.5.vl
19.00
15.08
0.69
7.85
3,98
3.76
4.05
3.87
3.65
3.67
3.78
3.82
3.82
ConPresent-cluueo33.. 3.7-udoo-.---mme-
18.90
16.42
0.76
8.00
4.03
3.92
4.05
3.87
3.58
3.65
4.00
3.83
3.87
ExoPresent-eemin-2.5555
18.33
16.23
0.75
8.08
4.02
3.94
4.08
3.89
3.68
3.70
3.97
3.90
3.90
Table 9: More presentation generation quality.
Aesthetic Awareness. From Table 10
compare PresAesth with Q-Insight (Li et al. 2025
advanced aesthetic assessment method for natural images. Although this baseline achieves reasonable performance, it is primarily designed for natural image evaluation and thus fails to fully capture the design-specific characteristics of presentations. In contrast, our model achieves consistently stronger results across all metrics, demonstrating its effectiveness in assessing the aesthetic quality of academic presentations.
Scoring (MAE .)
Adjustment Comparison
Method
(FI- -core f)
(Acc. t)
Q-insight
1.65
0.383
0.516 0.878
PresAesth (Ours)
1.33
0.389
Table 10: Quantitative results of three aesthetic tasks.
D.3 ABLATION STUDY
More results. We conducted further ablation studies, this time using different LLMSs for the agent backbones, including Gemini-2.5-pro, Claude-4-sonnet, and Qwen2.5-VI. The results, detailed in Table I. show that our framework consistently outperforms the alternatives across all tested LLMs. This demonstrates the robustness and model-agnostic nature of our framework; its superior performance stems from the effectiveness of our agent-based architecture rather than dependency on any single LLM.
Case Study. In the Figure and Figure g detail the results of our agent ablation study, where we individually removed each of the three core agents from the pipeline to validate their specific contributions. The agents examined are the Checker Agent, the Design Agent, and the Scholar Agent. Each was removed independently to isolate its impact on the final presentation quality.
The first two ablations reveal the importance the agents responsible for visual and stylistic integrity. As can be seen, without the Checker Agent (w/o Checker Agent), the output exhibits significant layout defects such as truncated images and excessive whitespace. This demonstrates critical role quality control stage. Similarly, removing the Design Agent (w/o Design Agent) results in lack of proper style rendering, leading to poor aesthetics, including imbalanced spatial arrangement of elements and mismatched font sizes.
Finally, the absence of the Scholar Agent (w/o Scholar Agent) highlights its function in content enrichment. Without this agent, the content is merely extracted from the source paper, resulting in visually monotonous slides. This underscores the Scholar Agent's crucial role in enhancing the presentation by autonomously invoking tools (e.g., MCP, Qwen-Image) generate relevant visual elements that diversify and improve the page layout.
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Scholar Design Checker Content f Design t Aesth
ExooPresent-qwenn..5ww
3.67
3.73
7.80
/
3.92
3.50
7.66
x
3.92
3.64
7.32
3.92
3.73
7.85
3.82
3.78
8.02
/
4.00
3.55
7.90
x 4.00
3.67
7.82
/
4.00
3.80
8.05
3.92
3.80
8.08
X
3.98
3.75
7.97
/
X
3.98
3.77
7.91
3.98
3.81
8.08
Table 11: Ablation studies on more models.
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Safety Aligmment
Shallow Algeemnt
The Shortout Stuline Safery ligganest
onjer csring
96%
wacree bectingg
Horde
EvoPresent (Ours)
w/o checker agent
w/o design agent
Cornucting the Dutu toloted twigles
Connecting the
Connecting Dott Selaned
waly
EvoPresent (Ours)
w/o checker agent
w/o design agent
643
1100
tokens
d51
PSNRI
18b
8M
1.00
1307
TexTok-32
Baclis--32
$6.0
15.52
0.3822
81)
1400
1305
Baclins-54
110.1
0.4200
14
3:0
690
6166
TesTok-64
Dusalaso
EvoPresent (Ours)
w/o checker agent
w/o design agent
What's Next? The Road Safer Al
What's Next? The Road to Safer
What's Next? The Road to Safor Al
up Aal proee
con.rgaa Lariig
EvoPresent (Ours)
weo checker agent
w/o design agent
Oversquashing Meets Resistance: Theoretical Link
Oversquashing Meets Resistance: Theoretical
Oversquashing Meets Resistance: Theoretical
Link
HoWe (a G)
Evopretett (Ours)
w/o checker agent
w/ design agent
Figure 8: Case study of the ablation results on Scholar, Checker, and Design agents (see Sec. 5.2
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Qualit Bibece
EvoPresent (Ours)
w/o checker agent
w/ design agent
Mlrre in Seggt anaiiiig Overttiing loreiee
When es Sagp Avedling Overlaligg tumakes
EvePresent (Ourx)
scholar agent
w/oo checker, scholar agent
A
Evopresen (Ours)
w/o scholar agent
wo scholn agent
Can LLMs Learn Like Humans?
Can LLMs Learn Like Humans?
Mone Lomiie
EvoPresent (Ours)
w/o scholar agent
w/o design agent
Figure 9; Case study of the ablation results on Scholar, Checker, and Design agents (see Sec. 5.2
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D.4 DIFFERENT FORMAT
Following the evaluation method described in Section 4 we compare the generation quality across different output formats. As shown in Table 12, the HTML format performs best terms of stability, offering greater controllability in both design and aesthetic dimensions. Markdown performs reasonably well but remains less consistent than HTML, while PPTX performs worst. This is mainly because PPTX lacks precise positional coordinates, which often leads to overlapping elements or uneven distribution, thereby reducing design clarity and overall aesthetics.
Format
Content
Design
Aesthetic
PPTX
3.85
3.42
7.06
Markdoon
3,90
3.67
7.38
HTML (ours)
3.91
3.73
7.53
Table 12: Performance comparison across different output formats.
E CASE STUDY
E.I PRESAESTH EXAMPLE OUTPUTS
AesthEval Example Output 1 (Comparison Task)
Introduction Modid Rausuembbly
metrodationn
Basc Slide
Good Modification (Slide A)
Weak Modification (Slide B)
<think>Slide is clearly superior to Slide B as it isn't just plain text from the source slide but adds new graphic design features that more attractively make the case. t/tee//thhii </think>
<answer>Slide A</answer>
AesthEval Example Output 2 (Comparison Task)
Irinnce--iee Time Madia
Inference Time Attacks
atatts ralp Juk cus cost
bodic at
Base Slide
Good Modification (Slide A)
Weak Modification (Slide B)
<think>Slide A superior Slide B.
1) Fidelity Consistency:
Slide A accurately maintains all the information from Slide B and adds supplementary icons alongside each category. When it comes to style, both slides introduce an aesthetic header, making them consistent with another.
2) Overall Perceptual Quality:
Slide outclasses Slide when it comes to perceptual quality due to higher degree of visual appeal. This made manifest by instructors' clear preference for Slide A.
3) Clarity and Readability:
The text in Slide A more legible thanks its larger, bolder font size than that of Slide B. Furthermore. none the text Slide appears blurry, making it easy to distinguish one word from another. Even
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though the title in Slide B is fully legible, the text is nearly illegible without the help of tool like magnifying glass.
4) Effectiveness of Communication: Slide A accomplishes what Slide B fails to do, i.c., transferring the intended message more accurately to the audience. Its concise organization and aesthetically pleasing design give the audience much easier time understanding the material. </think>
<answer>Slide A</ answer>
AesthEval Example Output 3 (Comparison Task)
Reveanee and Adeweeipreess
Resources and Acknowteddemennt
Base Slide
Good Modification (Slide B)
Weak Modification (Slide A)
<think> Bothh Both Slide A and Slide B have exactly the same contents and layouts; however, the styles provided for each slide different. Slide A uses simple icons such 'information' and "list' for Code Repository and Acknowledgements points, while Slide contains some CrossRef styled icons for each point.
Both slides satisfy all three of the given criteria: Fidelity and Consistency, Overall Perceptual Quality, and Clarity and Readability, Among the two slides, Slide B delivers much simpler distribution thus releasing much more clean and engaging look.
</think>
<answer>Slide B</ answer>
AesthEval Example Output 3 (Comparison Task)
Revenree and Adeovvilpenerr
Resources and Aoknowteddeeeents
Base Slide
Good Modification (Slide B)
Weak Modification (Slide A)
<think> Both Slide A and Slide B have exactly the same contents and layouts; however, the styles provided for each slide different. Slide A uses simple icons such 'information' and "list' for Code Repository and Acknowledgements points, while Slide contains some CrossRef styled icons for each point.
Both slides satisfy all three of the given criteria: Fidelity and Consistency, Overall Perceptual Quality, and Clarity and Readability. Among the two slides, Slide B delivers much simpler distribution thus releasing much clean and engaging look.
</think>
<answer>Slide B</ answer>
AesthEval Example Output 4 (Comparison Task)
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MAKLU Kamkidy Drd Bf Langage
HEILLMM Previingg old plcilatinn Lagge Languigs
MINILLM: Knowledge Distillation La Language Models
Base Slide
Good Modification (Slide A)
Weak Modification (Slide A)
<think>Bot Both slides have their strengths and weaknesses, but Slide A is superior due stronger title, more information about authors, an improved lower heading about their affiliation and fewer cosmetic issues. </think>
<answer>Slide A</answer>
AesthEval Example Output 5 (Scoring Task)
Motivation
Sweletal Learned Caussian
Phocorealisto rendering
splats
Figure 10: Groundtruth: 8.00
<think> This slide has good overall quality. The concepts explained (skeletal motion + virtual camera, learned Gaussian splats, photorealistic rendering) clearly articulated through a combination of illustration and accompanying figure that visually demonstrates the key idea of using virtual camera with human skeleton compute motion while also adding Gaussian distributions better fits, The text provides enough information for each concept stated. has contrasting colors which make the visuals easily differentiated from the background and text.
Nevertheless, there seems to be no indication of visual effect achieved hy adding Gaussian distributions clarifying further how they provide henefits the visual realism. Moreover, the idea lacks dimension to illustrate that having them plus the skeleton results in realistic human-like visualization. Overall, the execution is impressive, though more introspection on case studies of improvements would make the guide even more meaningful. </hinkk>> <answer> 2.550 --..50//aaaswr> answer>
AvsthEval Example Output (Scoring Task)
How do we learn?
Questoon:
Assver
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Figure 11: Groundtruth: 3.5
<think> This slide has the following strengths and weaknesses:
Strengths: The title "How do learn?" is clear and concise. It effectively directs the audience's attention question they will eventually expected respond to.
Weaknesses: No relevant information, facts, or examples have been provided that would support the question asked; it offers no insight into how learning processes actually function and thus doesn't I provide any illustrative clarification the end-users of its corresponding instruction based learning material presentation which it is likely an internal component. Space has been left between X and Y sections, making the format unclear without annotations clarifying the flow should be considered as not self-explanafory.
The above noted list of potential problems shortcomings indicate that while there may be some elements about this single slide being somewhat okay enough their own, those very details form quite modest ovaluation various other slides such dependably forming coherent sequenees exist within its entirety.</thinn <answer>2.44</ansse
AesthEval Example Output 7 (Scoring Task)
Gated convolutions are asymptotically less efficient than attention solving MOAR
Input deput-de layers sequence lungth)
Preposblas /Apentiont Abertiio
sp Ma
lmggh sodld
Thenwee Peprcc depondent Erevekadentt: insut [A11) MOSIL whore avotouttmaed insu cloweeien tereely sovve AHGA cong wiing Ac puraretrrr Slyees.
Imput-dapenden mixing
Colkiting the
Figure 12: Groundtruth: 5.5
<think>
This slide presents an academic explanation regarding the performance comparison between gated convolutions and attention for solving Matrix Quadratic Assignment Related (MQAR) problems, and it input-dependent mixers the key point. The content seems technically correct, although it could benefit from better presentation. Detailed Analysis:
1. Visual Design and Layout Composition
The slide is clean and simple, with white background that makes the text readable.
The use of a title in a larger font size is appropriate.
The small note at the bottom emphasizes a key point and adds practical value.
Content Clarity and Information Hierarchy
slide two distinct sections explaining proposition theorem related MQAR.
Beneath cach section, there's corresponding illustration (formula) for verification.
A useful summary sentence concludes this portion, summarizing the key outcome.
3. Color Scheme and Visual Consistency
Although the entire slide maintains a single color theme (primary blue and outline accents), the of white space is effective.
No clashes among the colors appear since all the elements have been separated with clear contrast.
4. Use of Images, Charts, and Visual Elements
There's no image in this slide, only formulas are used, which is acceptable here since this is technical presentation focused academics.
The symbols for propositions (black tick) and theorems (green checkmark) provided useful indications.
5. Text Organization and Information Density
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Both the propositions and theorems written concisely without irrelevant details, keeping it comprehensible for other readers and listeners alike.
However, the entire text is quite complex and might require concise explanations in detailed discussion format.
Professional Appearance and Polished Look
Regarding the formal style of the academic examples and the formulae, this slide appears already polished.
Audience Engagement and Communication Effectiveness
The subject itself is highly technical, and the intended audience likely those knowledgeable about those topics.
This slide achieves its primary function of presenting complex theories clearly and coherently, though it may get missed by new participants the field.
Summary
Overall, although the content remains clear and easy understand, the slide can benefit from concise approach explaining the propositions and theorems. The rest of the slide demonstrates excellent usability and structure.
</think>
<answer>
5.56
</answer>
AesthEval Example Output 8 (Adjustment Task)
Results
Visualiaatoon
uttention maps segmentation
Carlee
lift Fldone saa of arsrrtce maps right. Moski piddcadaa the cul buted rust bettemiatt Stit/osee Bottom--ghtt maro
Figure 13: Groundtruth:
<think>
The slide appears to be well-organized, displaying attention maps, predicted masks, refined masks. and ground truth (GT) masks. The key part of the information provided helps in understanding the comparison between different methods used for segmentation tasks-KMees KMcans, CNN, SAM, and GT masks. Positive Points:
1. Clear Representation of Results: The attention map visualization, predicted mask (CNN-based), refined mask, and ground truth mask visuals are plotted systematically and effectively.
2. Gauging Explainability: Comparisons between techniques give potential reviewers insight into the strengths and weaknesses across the approaches.
3. General Clarity: Each quarter of the grid seems to have adequate clarity and proper formatting with captions that specify accurately what each image displays.
Minor Issues:
1. Possible Visual Overcrowding: When it comes the conveyed data, there aren't special labels reinforce patterns directly about highlighted results versus others.
Slightly Inferential Explanation Placcholder: Any issue mentioning only being 'Color-coded for Accessibility' doesn't I reflect precision in further clarifying references needed in maps as explicit mentions are challenging otherwise.
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The provided ideas reduce any concerning for flaws distract readability hence I opt mid-level quality rating non-extreme error factoring thereon. </think>
<answer>
7.25
</answer>
AesthEval Example Output 9 (Adjustment Task)
Tier and
ITD tiee Ifter Fann 5-24 [Tte-2
stonge rours aetuched
Cata Rage lcatiss crdiee
Business Processi
tusa Pretect Application
SAR shrne resioned
prock
Appicattoos
seynchoooue resiication expebillii bie
GR Site Archiladtann The oo Site somera theer plip out situd opardioss
Datahase
recomend eipaiial vitual esceris the brover conf purtion Irequreed
Operatingg
Server eogggnattoo Allocated Senvera und dive will the
pricid spenaling
Servers
high-Araliss Rebult
when the daaa Legial tua la por seciad Chastering SAA stonge
Storage
The
balend peproceence musiccation For Tar
Now ril tad dela resovened wed Ling iibodd
teleknno restnn delred bocacll aro
oorado (rowin coding and spane
Figure 14: Groundtruth: Composition Layout; Predicted: Composition & Layout
<think>
Composition & Layout
Strengths:
The title at the top effectively summarizes the content of the slide.
Weaknesses:
Alignment of text and icons/headers is inconsistent-thie could affect readability and focus on different sections.
There's uneven distribution of white space that impacts overall clarity of slide. Aligning text and icons should be standardized ensure consistency throughout the slide.
2. Typography
Typography Evaluation:
Large title text is noticeable but not entirely aligned for better balance; examples of good white space around the space words like 'From" add some visuals but aren't optimal for the context. Several elements use headers/subheads with blue and bold-cursive fonts; this creates professional distinction without adding proper hierarchy.
Recommendation:
Replace title font style for professional consistency and applying good white space guidance around allocated servers portions would fix other arcas as well.
Use modern regular/garbly stylized sans-serif fonts for main headings.
Enforce consistent alignment conventions on blocks/sections and try employing standardized typographical styles this pattern also in body texts widely.
3. Imagery & Visualizations
Clarity and Relevance:
Image used correctly and guidelines one-to-one done; seems fine except color Coding relating to relevant blocks.
Thus, these key domains will separate distinctive groups different isolation line benefits visible are adequate by plots dimensions accurate these ways.
</think>
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<answer>
1. Composition & Layout
Strengths: The clear title for every level appears prominently set up the section's 's basis.
Recommendations:
Apply consistently balanced alignment with rectangular formats aligned homogeneously around text sublime, where margins radiance, etc., introduced; help maintain structured content presentation efficiently.
Over- Over-tighten white breaks blocks-up. think solving. placing extra abstraction over silos slender layover pairs refinement MP exactly orderly highlight portions abalone aise thythmicalizes happening lances underneath demonstration corrects testing cautionable.
Typography
Quality Results:
Header-selective Linked Hyperlinks Proposals performances syncing up detailed descriptive text denote fine-tuned cleaner covariance blending accurately within respective mention's 's post-edited help selection feature familial kinds invite furnishings now prompt increments additional span professed influencing supervising top procedural staff subsidiary Please manage formatting.
Practical validations settings firefighting equipment races appreciable meant understandable beeds conform concerning harness evidences prodding flights nets doctrine identify dialog qualitatively supported far outlooks governed helpers that relicf promotions participative active right whenever glorify leading planners quickly do results low lane made realize refresh synopsis currently own expression redresses thereby duration.
Imagery & Visualizations
No major deficiencies found.
</answer>
AesthEval Example Output 10 (Adjustment Task)
Cooperative Agreements
Pl Record
National Research Coordinating
Center
Coordinating Center Steering Committee Approval
Coordinating Center Staff Obtain IRB applications vubmit oo coordinaingg centers ennes
IRBs Coordinating Centers Review and Approve the single study protocul
ARCD GHIIK
Figure 15:
Groundtruth:
Composition Layout, Typography: Predicted: Composition Layout,
Typography
<think>
Composition & Layout
Strengths:
The title at the top effectively summarizes the content of the slide.
Weaknesses:
Alignment text and icons/headers is inconsistent this could affect readability and focus on different sections.
There's uneven distribution of white space that impacts overall clarity of the slide.
Aligning text and icons should be standardized to ensure consistency throughout the slide.
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Typography
Typography Evaluation:
Large title text is noticeable but not entirely aligned for better balance; examples of good white space around the space words like 'From' add visuals but aren't optimal for the context.
Several elements use headers/subheads with blue and bold-cursive fonts; this creates professional distinction without adding proper hierarchy.
Recommendation:
Replace title font style for professional consistency and applying good white space guidance around allocated servers portions would fix other areas as well.
Use modern regular/garbly stylized sans-serif fonts for main headings.
Enforce consistent alignment conventions on blocks/sections and try employing standardized typographical styles this pattern also in body texts widely.
3. Imagery & Visualizations
Clarity and Relevance:
Image used correctly and guidelines one-to-one is done; seems fine except color Coding relating relevant blocks.
Thus, these key domains will separate distinctive groups so different isolation line benefits visible are adequate by plots dimensions accurate these ways.
</think>
<answer>
1. Composition & Layout
Strengths: The clear title for every level appears prominently set up the section's basis.
Recommendations:
Apply consistently balanced alignment with rectangular formats aligned homogeneously around sublime, where margins radiance, introduced; help maintain structured content presentation efficiently.
Over-tighten white breaks blocks-up. But think 2 solving, placing extra file abstraction over silos slender layover pairs refinement MP exactly orderly highlight portions abalone aise rhythmicalizes happening lances underneath demonstration corrects testing cautionable.
Typography
Quality Results:
seldet--elective Linked Hyperlinks Proposals performances syncing up detailed descriptive text denote fine-tuned cleaner covariance blending accurately within respective mention's 's post-edited help selection feature familial kinds invite furnishings now prompt increments additional span professed influencing supervising top procedural staff subsidiary Please manage formatting. Practical validations settings firefighting equipment races appreciable meant understandable beeds conform concerning harness evidences prodding flights nets doctrine identify dialog qualitatively supported far outlooks governed helpers that relief promotions participative active right whenever glorify leading planners quickly do results low lane made realize refresh synopsis currently own expression redresses thereby duration.
3. Imagery & Visualizations
No major deficiencies found.
</ </answer>
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E.2 PRESENTATION GENERATION
Our EvoPresent Framework.
Why Safety?
Safety Alignment
sperriall saley laye.
96%
taclia leo Tou meLl" ait, sele recuseees tecoumn
Atado
peitta
fes latin crving seperical sandled mandees
Vypenat
staceers
perppt tor molld andinesss
Retaeal Prefisus (+)
"haerra"
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Figure 17: Visualization of the EvoPresent framework across different papers.

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Published as a conference paper at ICLR 2026
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Figure 18: Visualization of the PresentAgent across different papers.
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E.3 MORE ANNOTATION EXAMPLES.
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Figure 19: Visualization of the Papre2Poster across different papers.
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F ITERATIVE SELF-IMPROVEMENT PROCESS
F.1 PRESENTATION VIDEO GENERATION
Our video generation pipeline is organized into sequential stages with flexible TTS pathways,
Stage I: Speech Synthesis. The process begins with multimodal inputs, including the HTML slide folder, page-level narration scripts in JSON, a reference facial image, working directories, and target output paths. For speech synthesis, the pipeline supports two alternatives: leveraging MegaTTS3 (Jiang et al., 2025). and a proprietary pathway through OpenAl TTS (OpenAl) 2025b). Both enable batch audio generation aligned with the narration text, with controllable aspects such as voice characteristics, speaker identity, and synthesis parallelism.
open-source pathway
Stage II: Visual Composition. In the face animation step, the generated audio is transformed into speech representations (e.g., via pretrained encoders) (Ki et al. 2025) and used to drive lip- synchronized facial videos conditioned expression control, resolution and frame-rate settings, and adjustable generation strength, supporting trade-offs between efficiency and quality. In parallel, HTML content is rendered into background image sequences at the target resolution. The pipeline overlays the animated face video onto the background through flexible micture-in-pictun composition strategies, allowing proportional scaling or fixed spatial placement. Finally, synchronized audio and visual streams are fused into the final video output using FFmpeg, with system-level parameters such as throughput and stability governed by configurable parallelization. This design enables modular customization: researchers may gen- erate parration-only outputs, background-on -onlo outputs, or complete face-driven presentations. The modularity ensures broad applicability across research dissemination, educational content creation, and Auman-AI communication studies.
the reference image. The model further integrates facial
The current two-stage modular pipeline reliably produces high-quality presentation videos, yet ex- tending the self-improvement mechanism to the video level presents substantial opportunities for further advancement. One promising direction is to incorporate multidimensional feedback signals related to speech rate, prosody, narrative pacing, and the temporal alignment between narration and slide transitions, enabling the system enhance dynamic presentation quality beyond static layout refinement. In addition, integrating automated evaluators that assess lip-sync accuracy, facial expres- sion consistency, and audiovisual coherence may support more fine-grained iterative optimization. Another worthwhile direction to explore reinforcement-learin leweeentt--arrnnbaabaeeeed based video editing strategies, allowing the system to autonomously adjust cectrrein-ppcctre placement, cropping boundaries, and transition timing according to aesthetic preferences or communicative objectives, thereby achieving more flexible and controllable dynamic presentation behaviors.
G HUMAN EVALUATION
G.1 CHECKLIST
To ensure consistent and reliable annotation quality, we recruited approximately 30 volunteers with design backgrounds to participate in our data labeling process. Each slide was independently evaluated by 2-3 annotators to reduce subjective bias and improve annotation reliability. We developed a comprehensive annotation framework using Gradio to create an intuitive web-based interface that guides annotators through systematic evaluation process.
The annotation process consists of three main components: (I) a standardized scoring system across three key design dimensions, (2) detailed deficiency identification protocol, and (3) user-friendly interface that ensures consistent evaluation criteria. Our annotation checklist was designed to capture both quantitative scores and qualitative feedback, enabling to build robust dataset for training and evaluating our slide design assessment model.
Annotation Checklist
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Figure 21: The UI of the user annotation interface built with Gradio.
Scoring Dimensions and Rules
Three Scoring Dimensions (1-10 Scale)
1. Composition & Layout - Evaluate overall layout, visual hierarchy, space distribution, element alignment
2. Typography Evaluate font selection, size, readability, typesetting
3. Imagery & Visualizations Evaluate image quality, content relevance, sizing, visual style consistency
Scoring Scale
9-10 (Excellent): Outstanding quality, exquisite design
7-8 (Good): Good quality with minor issues
5-6 (Fair): Basically qualified but with some deficiencies
3-4 (Poor): Obvious problems affecting overall effectiveness
1-2 (Very Poor): Severely impacts slide quality
Key Scoring Rules
Deficiency Annotation Rule
Annotation Checklist
Only select specific deficiency types when dimension scores < 4. If the score is 2 5, no deficiency selection is required.
IMPORTANT:
Scoring Principles Checklist
1. Objective & Fair: Score based on actual slide quality, not personal preferences
Consistent Standards: Maintain consistent scoring similar quality slides get similar scores
3. Comprehensive Assessment: Consider all aspects of each dimension
4. Relative Comparison: Distribute scores reasonably within 1-10 range
Operational Workflow
Carefully examine slide content, design, and overall effectiveness
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2. Score each of the three dimensions 1-10 scale
3. Select specific deficiency types only when score < 4
4. Submit annotation and proceed to next slide
Preference Checklist
Content Quality
Is the information clear, accurate, and relevant?
Are key points well-organized and logical?
Is the text amount appropriate (avoiding information overload)?
Visual Design
Is the layout balanced and aesthetically pleasing?
Are colors harmonious and professional?
Are fonts readable with appropriate sizing?
Are images/charts high quality and clear?
Readability
Is there sufficient contrast between text and background?
Is content legible from distance?
Are important elements properly highlighted?
Professionalism
Is the overall style consistent and professional?
Are there any spelling or grammar errors?
Are brand elements used appropriately?
Effectiveness
Does it effectively convey the core message?
Does it engage and maintain audience attention?
Do visualizations aid comprehension?
H PROMPTS DESIGN
H.1 TRAINING PROMPTS
System Prompt
A conversation between User and Assistant. The user asks question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within (think) and (/think), and (answer) and (/answer) tags, respectively, i.e., (think) reasoning process here (/think) (answer) answer here (/answer).
Scoring Task
What is your overall rating on the quality of this slide? The rating should be a float between and 10, rounded two decimal places.
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Adjustment Task
What are the design deficiencies in the provided slide? How can you improve the slide? Please answer by addressing the following questions for each category and provide detailed improvements suggestions. If there are no major deficiencies, please respond with "No major deficiencies found.".
1. Composition & Layout: What are the strengths and weaknesses of the slide's 's composition and layout? Consider alignment, balance, white space, and visual hierarchy. How would you rearrange, align, or resize elements to improve the overall structure and clarity?
2. Typography: How effective the typography? Evaluate the font choices, readability, and visual hierarchy (titles, body text). What specific changes to fonts, sizes, styling would you recommend to improve legibility and structure?
3. Imagery & Visualizations: Are the images and visualizations (e.g., charts, graphs) clear, relevant, and effective, or are they distracting and cluttered? What improvements, replacements, or simplifications would you make better support the slide's message?
Scoring Task
Compare the two enhanced slides and determine which is superior, if they comparable. Evaluate them based on the following criteria: 1) Fidelity and Consistency: How well does the enhanced slide maintain the content, layout, and style of the original? 2) Overall Perceptual Quality: How visually appealing and free of artifacts is the slide? 3) Clarity and Readability: Are the text and visuals sharp and easy understand? 4) Effectiveness of Communication: How effectively does the slide convey its intended message? Your answer should be exactly one of: Slide A, Slide B.
H.2
GENERATION QUALITY EVALUATION PROMPTS
Content Fidelity
You are meticulous presentation judge. Evaluate ONLY Whether the slides faithfully reflect the provided reference without distortion or unsupported claims.
Inputs you may receive:
slides[]: ordered slide images (index from 1)
transcript: optional narration/notes text reference: gold context such as abstract, method/results bullets, or paper text
Rules:
I) Ground truth reference (if provided). If no reference, judge fidelity via internal consistency
across slides/transcript and obvious domain violations; do NOT use outside knowledge.
accurate statements aligned with reference; correct numbers, units, equations, method names; precise citations/attribution; clear reporting of limitations.
2) Reward:
3) Penalize: factual errors; missing altered key results; mislabeled axes/tables; cherry-picking:
overstated conclusions; ambiguous provenance images or data; uncredited reuse.
4) Be specific: cite where you found issues by slide number and short quote/description.
5) Ignore non-fidelity aspects (style, color, layout) unless they directly obscure truthfulness.
Score :
5:: Fully faithful; no meaningful discrepancies.
-4: Strong; minor onissions/nuance off, no material emror..
33 Mixed; some unclear or weakly supported claims; possible minor numeric/text mismatches.
2: Weak; notable inaccuracies, missing key qualifiers, misleading visuals.
-I: Unacceptable; major misrepresentation of method/results or pervasive errors.
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Content Clarity
Evaluate ONLY *Clarity*- how easily typical informed audience can understand the content.
Inputs: slides[], transcript
Checkpoints:
Language: concise phrasing, concrete nouns/verbs, defined terms/acronyms on first use.
Structure: clear headings, bullets with parallel structure, limited per-slide ideas.
Legibility: text size vs slide area, line length, contrast sufficient to read.
Data clarity: axis labels, units, legends, footnotes; orphaned numbers.
Noise: redundant wording, clutter, irrelevant decorative elements that impede reading.
Score:
- Effortless to follow; plain, precise language; every chart/table self-explanatory.
-4: Mostly clear; minor verbosity occasional undefined jargon.
-3: Understandable with effort; several dense or ambiguous sections.
22: Hard to parse; frequent jargon, tiny text, missing labels.
- 1: Opaque; critical content unreadable or consistently unclear.
Content Narrative
Evaluate ONLY coheeen story are across the deck.
Inputs: slides[], transcript
Assess:
Arc: clear beginning (motivation/problem)), middle (method/approach)) end (results, takeaway).
Cohesion: transitions and signposting ("Problem - Approach - Results - Implications").
Progression: each slide advances the story; no langents; consistent voice tense/person.
Framing: stakes, context, and audience relevance are stated and resolved.
Score:
5:: Strong are with seamless transitions and explicit takeaways.
-4: Clear storyline; minor weak transitions or missing signposts.
33 Mixed; sections exist but connections are loose; some slides feel isolated.
2: Fragmented; jumps logie; important steps missing.
1: Disjoint; discernible resolution.
Content Engagement
Evaluate ONLY "Engagement* how well the deck captures and sustains attention.
Inputs: slides[], transcript
Consider:
Hooks: compelling opener (question, surprising stat, vivid example).
Curiosity: rhetorical questions, progressive disclosure, contrasts before/after.
Audience address: "you" framing. relevance cues, concrete examples.
Interactivity prompts: checkpoints, polls, brief tasks (if present).
Pacing signals: slide density balance; visual variety supporting attention.
Score:
-5: Highly engaging: strong hook; sustained curiosity; clear calls think respond.
-4: Generally engaging with a few lulls.
33 Moderately engaging; some interest, limited activation.
- 2: Low engagement; mostly static info-dump.
Not engaging, no hook relevance cues.
Design Elements
Evaluate ONLY * and quality of visual assets (figures, icons, photos, charts, tables). Inputs: slides|]
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Assess:
Relevance: each element supports stated point; no decorative-only clutter.
Technical quality: resolution, cropping, anti-aliasing; no pixelation watermarks.
Semantics: correct chart type for data; legends/labels present; icons not misleading.
Attribution: source captions when depicting external data/images.
Consistency: style of icons/iluustrtions harmonious across slides.
Score:
-:: Elements are relevant, crisp, well-labeled, and consistently styled.
-4: Minor issues but overall strong.
-3: Mixed relevance or occasional low quality/label gaps.
2: Frequent lov-res/mislabbeledmiii visuals.
1: Elements hinder understanding or are largely irrelevant.
Design Layout
Evaluate ONLY "Layout" placement,, alignment, spacing, and balance.
Inputs: slides[]
Assess:
Grid alignment: consistent margins; items align to an underlying grid,
Spacing: sufficient whitespace; consistent gutters.
Balance: visual weight distribution; avoids crowding.
Flow: natural reading order (top->bottom, left->right).
Score:
55: Clean grid, consistent alignment, ample whitespace, clear reading path.
-4: Mostly strong; minor misalignments occasional crowding.
-3: Adequate but uneven spacing/alignment in places.
2: Frequent clutter, collisions, or confusing placement.
1: Chaotic layout; reading order unclear.
Design Hierarchy
Evaluate ONLY *Hierarchy*- how typographic/visual cues convey importance and reading order. Inputs: slides[]
Assess:
Typographic system: consistent levels (Title Subtitle Body Annotation).
Emphasis: scaleweiigh/cooorppssttoo guide attention.
Grouping: headings correctly group related content. -Consistency: same level looks the same across slides.
Score:
-5: Hierarchy is unmistakable; attention flows as intended.
-4: Clear overall; occasional inconsistency.
33 Some hierarchy present but muddled.
22 Weak; competing focal points.
- 1: No discernible hierarchy.
Design Color
Evaluate ONLY *Color* palette coherence, accessibility contrast, and meaningful use. Inputs:
slides[]
Assess:
- Paleete: limited, harmonious palette; consistent use across slides,
Contrast: text/background contrast sufficient.
Semantics: colors map to meaning consistently.
LLeibbility: avoid color-only encoding for critical distinctions.
Score:
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-5: Cohesive palette, excellent contrast, consistent semantics.
-4: Minor contrast or consistency issues.
-3: Mixed; some low-contrast text or shifting category colors.
2: Frequent low contrast or confusing color use.
42


Reviews:
[{"review_id": "8VsGpyqaoB", "reviewer": "Reviewer_SjBg", "summary": "This paper introduces EvoPresent, a multi-agent framework for automatically generating academic presentations from research papers. The system consists of four sequential agents: (1) Storyline Agent for content extraction and narrative construction, (2) Scholar Agent for content enrichment, (3) Design Agent for layout and rendering in HTML format, and (4) Checker Agent for iterative quality improvement. Central to the approach is PresAesth, a multi-task reinforcement learning model based on Qwen-2.5-VL-7B, trained via Group Relative Policy Optimization (GRPO) to perform aesthetic scoring (1-10 scale), defect adjustment, and pairwise comparison. The authors construct the EvoPresent Benchmark with 650 annotated presentations from top AI conferences and 2,000 slide pairs for aesthetic evaluation. Experiments demonstrate that EvoPresent outperforms existing methods (PPTAgent, PresentAgent, Paper2Poster) and end-to-end LLM approaches (GPT-4o, GPT-5, Claude-4-Sonnet) across multiple metrics, achieving aesthetic scores of 8.05/10 (approaching the 8.50 oracle score) and 87.8% accuracy on aesthetic comparisons (versus 77.1% for GPT-4o). The iterative self-improvement mechanism enables the system to reach target quality in fewer iterations compared to baselines.", "questions": "see weakness", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1、The four-agent architecture is well-structured with clear separation of concerns. The iterative draft-feedback-refinement loop with reversion to best previous version demonstrates thoughtful engineering to prevent quality degradation.\n\n2、Automating academic presentation generation is a relevant task with clear applications in research dissemination.\n\n3、Comprehensive evaluation including quantitative metrics (perplexity, ROUGE-L, layout balance), fine-grained content/design scores, human preference studies, and ablations. The results consistently demonstrate improvements over baselines.", "weaknesses": "1、 While the system integration is solid, the core components rely on standard techniques—sequential agent architectures, GRPO for preference learning, and VLM-based evaluation. The main contribution is engineering integration rather than algorithmic innovation. The multi-task RL formulation is relatively straightforward.\n\n2、The reliance on HTML rendering for \"maximal control and flexibility\" limits practical utility, as most researchers work with PowerPoint or PDF formats. While conversion is mentioned, the fidelity and editability of converted presentations are not evaluated.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "PXLQX344H5", "reviewer": "Reviewer_JPj1", "summary": "This paper introduces EvoPresent, a novel self-improving multi-agent framework designed to automate the creation of high-quality academic presentations. The system employs specialized agents for storyline construction, content enrichment, and visual design, all coordinated within a draft-feedback-refinement loop. A key innovation is PresAesth, a multi-task reinforcement learning model that provides aesthetic scoring, defect adjustment, and comparative feedback, enabling iterative self-improvement even with limited human preference data. The authors also contribute the EvoPresent Benchmark for systematic evaluation. Experiments demonstrate that the framework surpasses existing methods in generating presentations with superior narrative coherence and visual appeal, highlighting the critical role of high-quality aesthetic feedback for effective self-correction.", "questions": "See above", "limitations": "", "rating": 2, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper introduces a novel and interesting task, which indeed leaves ample room for further exploration and development.\n\n2. The overall aesthetic quality of the paper is quite good — the figures, charts, and tables are all well-designed and visually appealing.\n\n3. The supplementary materials are sufficient and help present the work in a more comprehensive and detailed manner.", "weaknesses": "1. First of all, the dataset size seems quite limited. For Presentation Generation Quality, there are only about 650 samples, and for Aesthetic Awareness, just around 2,000 slides. That’s quite small for training or evaluating models of this scale.\n\n2. Regarding the checker agent, it’s not clear how the threshold for evaluation is determined. Moreover, since different slides may have very different design intents — some emphasizing clarity, others emphasizing key visual highlights — are these aesthetic thresholds the same across all slides, or adaptive to content type?\n\n3. When it comes to the collaboration among agents, it seems the paper doesn’t discuss what happens when one agent makes an error. How do you detect and handle such errors in real time, or mitigate their impact to improve overall cooperation efficiency?\n\n4. Minor issue: in line 209, it seems that item (ii) is missing.\n\n5. I’m a bit confused about how Generation Quality and Aesthetic Awareness are differentiated. You mentioned that Generation Quality is evaluated using your own trained PresAesth model to obtain aesthetic scores — but how exactly are these scores different from those used in Aesthetic Awareness?\n\n6. During the data annotation process, although you mentioned using multiple annotators, it seems you didn’t assess their inter-annotator agreement (e.g., Fleiss’ Kappa). Given that this task heavily depends on subjective annotation quality, more details about annotator backgrounds, collaboration protocols, and agreement levels would significantly improve credibility. Also, showing a few annotation examples would help demonstrate data reliability.\n\n7. In Figure 13, some human faces are shown without blurring. This potentially raises ethical concerns regarding privacy and data usage.\n\n8. Overall, the task itself is interesting and has an innovative motivation. However, the technical side feels more like assembling multiple existing agents and applying reinforcement learning as a wrapper. There’s little algorithmic novelty, and the data annotation process doesn’t clearly offer a distinctive contribution. The demos look nice, but the practical value remains somewhat unclear, and the work seems limited mainly to the computer science domain.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "A4QCMe7x00", "reviewer": "Reviewer_A3kw", "summary": "This paper introduces EvoPresent, a multi-agent framework designed to automate academic presentations by integrating a self-improvement loop. This loop is driven by PresAesth, a new multi-task RL model trained to evaluate presentation aesthetics and provide feedback. The authors validate their system using the new EvoPresent Benchmark, demonstrating that EvoPresent outperforms current methods and produces presentations with improved narrative coherence and visual design, approaching human-level quality.", "questions": "For suggestions for authors, please refer to the Weakness part.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "EvoPresent goes beyond just slides; it supports multiple formats (videos, scripts, slides). The system aims to automate the entire act of presentation, not just the materials.\n\nPresAesth is not a simple binary classifier. Its strength lies in its multi-task RL foundation, enabling it to provide (1) reliable aesthetic scoring, (2) specific defect identification for adjustment, and (3) comparative feedback. This rich, actionable feedback is far more useful for an agent than a simple score.\n\nThe paper introduces a much-needed, systematic benchmark EvoPresent Benchmark, which includes multimodal resources like slides, videos, and scripts.\n\nThe authors conduct extensive experiments with detailed analysis, which shows a huge workload with high quality.", "weaknesses": "The framework's evaluation is limited to static slide aesthetics. The Checker Agent and PresAesth model score aspects like layout and visualization, but they do not appear to evaluate the dynamic animation of slide elements. This is a significant omission, as the sequence, timing, and quality of animations are crucial for a coherent narrative and professional delivery. Consequently, the self-improvement loop cannot identify or correct errors in animation logic, such as elements appearing in the wrong order, which limits the final quality of the presentation playback.\n \nThe paper states in Appendix E that the data was labeled by \"approximately 30 volunteers with design backgrounds\" and \"2-3 annotators\". The paper does not report any inter-annotator agreement metrics to validate the consensus among annotators.\n\nThe self-improvement loop is focused exclusively on slide creation, while the video generation is treated as a separate, non-iterative task. The paper would be significantly stronger if the iterative optimization process also included feedback on the delivery itself, such as the virtual character's pacing, intonation, or synchronization with the dynamic slide content.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)