Pith. sign in

REVIEW 4 major objections 4 minor 2 cited by

PresentAgent: Multimodal Agent for Presentation Video Generation

T0 review · 4 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read PresentAgent introduces a modular pipeline that converts long-form documents into fully narrated, time-aligned presentation videos and reports that it approaches human-level quality on a vision-language-model-based evaluation.

desk verdict New task and benchmark, but the 'human-level' claim is contradicted by the paper's own 5-document evaluation; the central result is unsupported. read the letter →

arxiv 2507.04036 v1 pith:WOJDC4VQ submitted 2025-07-05 cs.CV

Jingwei Shi , Zeyu Zhang , Biao Wu , Yanjie Liang , Meng Fang , Ling Chen , Yang Zhao This is my paper
classification cs.CV
keywords document-to-presentationvideogenerationmultimodalagentslidetext-to-speechnarrationaudio-visualalignmentvision-languagemodelevaluationDoc2Presentbenchmarkpresentation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

PresentAgent is a new system for a task the paper names document-to-presentation video generation: taking a long document, such as an academic paper, web page, technical blog, or slide deck, and producing a narrated video in which slide frames and spoken audio are aligned in time, the way a human presenter would deliver it. The paper argues that existing tools stop at static slides or text summaries, and that a modular pipeline of outline planning, layout-aware slide rendering, narration script generation, text-to-speech synthesis, and video assembly can close that gap. To back the claim, the authors introduce PresentEval, a two-part evaluation that scores generated videos with a multiple-choice quiz answered by a vision-language model and 1–5 preference ratings for content, visual, and audio quality. On their curated set of 30 document–presentation pairs the system is said to approach human-level quality; the reported head-to-head table covers a five-document subset, a scope limit the authors attribute to API cost. If the claim holds, the practical payoff is that dense written material can be turned into accessible spoken-and-visual presentations without manual scripting, voice recording, or slide design.

What carries the argument

The mechanism that carries the argument is the slide–audio pair. Each semantic block of the document yields one slide $S_k$ and one narration audio $T_k$, and the video is the time-aligned composition of these pairs; alignment works because the narration is generated first and each slide is displayed for the duration of its own audio segment. Around this core sit two supporting components: a slide planner that maps content blocks to HTML-like layout templates with editable operations (replace text, insert image, add list), and PresentEval, a two-path scoring protocol in which a vision-language model answers five quiz questions per document and rates content, visual, and audio quality on a 1–5 scale. The paper also uses a dynamic routing policy across six LLM backends for generation and a TTS system with prosody controls, but the pairing of slide and narration is what makes the output a presentation video rather than a slideshow with voiceover.

What would settle it

Give the same document–video pairs to human viewers: have them answer the same five multiple-choice questions and rate content, visual, and audio quality on the same 1–5 scales, then compare their scores and answer patterns to Qwen-Omni-7B and Qwen-VL-2.5-3B outputs. Low correlation between model and human scores, or human quiz accuracy on PresentAgent videos falling below the human-authored video reference, would refute the approaching-human-level-quality claim.

Watch

Extended reading notes

Core claim

PresentAgent's central claim is that a document-to-presentation video can be produced by a four-stage generative pipeline, formulated as $V = \mathrm{Compose}(\{(S_1,T_1),\dots,(S_K,T_K)\}) = g(D)$: the input document $D$ is segmented into semantic blocks through outline planning; each block is matched to a layout template and rendered as a slide frame $S_k$; a language model rewrites the block's key message into oral-style narration, which a text-to-speech system turns into audio $T_k$; and the final video $V$ displays each slide for exactly the duration of its audio. The paper claims this output closely mimics human-style delivery, and that PresentEval's two-path assessment, fixed multiple-choice questions for factual comprehension plus VLM preference scoring along content, visual, and audio dimensions, shows the system approaching human-level quality across all measured metrics. The authors position this as the first fully integrated solution to the task, in contrast to prior work that generates static slides or isolated audio summaries.

Load-bearing premise

The claim that PresentAgent approaches human-level quality rests on scores produced by two vision-language models answering quizzes and rating videos, with no human rating study or correlation check to confirm those scores match how real audiences perceive the presentations; if those model scores diverge from human judgment, the human-level conclusion fails.

Editorial extensions

If this is right

  • Document-to-slide systems that today stop at static decks can be extended to fully narrated, time-aligned video by adopting the slide–audio pairing step.
  • PresentEval gives future presentation-video generators a scalable, reference-free scoring protocol: quiz accuracy plus VLM ratings, with no human annotation except the quiz questions themselves.
  • Because the pipeline is modular, replacing any stage, whether outline planner, slide renderer, narration model, or TTS engine, should improve the final video without redesigning the whole system.
  • The reported results imply that for at least some document types, an automated pipeline can produce videos whose factual content survives a comprehension quiz as well as a human-authored video does.
  • If the approach transfers to business reports, policy briefs, and technical manuals, the main cost of producing accessible narrated presentations shifts from human labor to compute and API usage.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper's split evaluation scores visuals and audio separately, which suggests a natural next test it leaves open: a fusion-aware scorer that checks whether narration and slides agree semantically would likely re-rank the six model variants.
  • A direct way to extend the effective-communication claim beyond quiz accuracy would be to measure real audiences' retention or learning gain after watching the generated videos; the paper does not run that study.
  • The five-document evaluation bottleneck implies that the diversity claims about the 30-pair benchmark are untested; a cheaper open-model pipeline could run the same protocol at full scale and reveal where the system degrades.
  • If PresentEval's VLM scores were validated against human ratings, the same quiz-and-preference protocol could be reused as a general benchmark for any document-to-multimodal generation task, not just presentation video.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. PresentAgent proposes a modular pipeline for converting long-form documents into narrated presentation videos with synchronized slides and audio, and introduces PresentEval, a VLM-based evaluation framework with objective quizzes and subjective scoring. The paper reports experiments on a curated benchmark and claims that PresentAgent approaches human-level quality across all metrics. The system itself is a four-stage pipeline (outline planning, slide composition, narration/TTS, video assembly), and experiments compare several LLM backends against human-created reference videos on quiz accuracy and 1–5 subjective scores.

Significance. If the claims were fully supported, this would be a useful step toward automatic generation of time-aligned, narrated presentation videos from long documents, a task that existing slide-generation and text-to-video systems do not directly address. The proposed benchmark of 30 document–video pairs and the two-path evaluation idea (factual quizzes plus VLM preference scoring) are potentially valuable resources for the community. The paper also has a sensible modular design with explicit control points, and the authors promise to release code. However, the current evidence does not substantiate the central 'human-level quality' claim: the evaluation is internally inconsistent about the test set size, the scoring models are not validated against human judgments, and no existing baselines are compared. The significance of the contribution is therefore currently contingent on substantial additional evaluation work.

major comments (4)
  1. [§5.1 vs. Table 3 vs. §7] The abstract and Section 5.1 state that the test set consists of 30 document–presentation pairs spanning four document types, but Table 3 is captioned 'Detailed evaluation results on the 5-document test set,' and Section 7 says 'evaluation was limited to five academic papers, potentially underrepresenting the document diversity shown in our benchmark.' These statements are irreconcilable as written. The headline claim of approaching human-level quality on the 30-pair curated dataset is not supported by the reported experiments, and the paper does not explain how the five documents were selected or why the other 25 were omitted. This is the single most load-bearing issue in the manuscript: the central claim must be either backed by results on all 30 pairs or explicitly restated to reflect the five-document evidence.
  2. [§5.1, §5.2, Table 2] PresentEval's subjective scores are produced entirely by Qwen-Omni-7B and the quiz answers by Qwen-VL-2.5-3B, with no human rating study, no inter-annotator agreement, and no correlation check against human judgments. The paper states that this enables 'human-aligned evaluation,' but that claim is not demonstrated. Moreover, Section 5.2 says Qwen-VL-2.5-3B-Instruct is also used inside PresentAgent for slide-layout self-critique, so the generator and the judge share the same model family and likely share systematic preferences. This circularity makes the 'approaches human-level' conclusion particularly fragile. The authors should validate PresentEval against human raters on at least a subset of videos, or report the correlation, before using it as the primary evidence for the paper's central claim.
  3. [Table 3, §5.3] Even on the five documents, the reported differences are within the noise of the evaluation procedure. Quiz accuracy is computed from five questions per document (25 questions total for the five-document set), so a one-question difference changes accuracy by 0.04; the human reference scores 0.56 and the best system 0.64, which is a two-question difference on 25 items. The subjective scores are single VLM judgments on a 1–5 scale with no variance, no repeated sampling, and no statistical test, yet the text interprets differences of 0.1–0.2 as meaningful (e.g., 'Gemini-2.5-flash scores highest in visual quality but lower in comprehension'). These numbers do not support the conclusion that PresentAgent 'approaches human-level quality' or that one backend is better than another.
  4. [§5, §8, contribution bullet] The paper claims in the contribution list that PresentAgent 'significantly outperforms competitive variants,' but the experiments contain no comparison to any existing document-to-slide or document-to-video baseline, and no ablation of the proposed modules. The only comparison in Table 3 is among different LLM backends inside the same PresentAgent pipeline. Section 5.1 explicitly says 'our focus is not on competing with existing baselines,' which contradicts the contribution claim. To support the stated contribution, the authors should compare against at least one strong prior method, such as PPTAgent or Doc2PPT adapted to video output, and include module ablations (e.g., with/without outline planning, with/without dynamic routing, with/without self-critique).
minor comments (4)
  1. [§4.3 vs. §3.1] Section 4.3 says each narration is controlled to 'typically between 30 and 150 seconds,' but Section 3.1 states that the reference videos are 1–2 minutes long and contain 5–10 slides. Five slides at 30 seconds each would already exceed two minutes, and ten slides at 150 seconds each would be far longer. The target-duration range should be reconciled with the stated video length.
  2. [References] The references list both Zheng et al. 2025a and Zheng et al. 2025b as the same PPTAgent paper; these should be merged into a single entry with consistent citation keys.
  3. [§5.1] The sentence explaining the split evaluation strategy says the reason is that no single multimodal model can assess 'videos longer than two minutes,' yet the paper also says each synthesized video is approximately two minutes. This reasoning is confusing and should be clarified or removed.
  4. [Table 1] The table contains a typo in the header: 'Prensentation' should be 'Presentation.'

Circularity Check

1 steps flagged · score 5.0 of 10

PresentAgent's headline 'human-level quality' rests on a VLM judge that is also used to critique and steer generation; the 30-pair claim is further contradicted by the paper's own 5-document limitation.

  1. other [Section 5.1 (Evaluation Setup), Section 5.2 (Implementation Details), Table 3]
    "In the Objective Quiz stage, we use Qwen-VL-2.5-3B (Wang et al., 2024b) to evaluate the accuracy ... In the Subjective Scoring stage, ... using Qwen-Omni-7B. ... we introduce the lightweight VLM Qwen-VL-2.5-3B-Instruct to score slide layout, chart readability, and cross-modal consistency, feeding its self-critique back into generation."

    The same model family that judges the outputs also participates in producing them. Qwen-VL-2.5-3B-Instruct is used inside PresentAgent to critique slide layout and cross-modal consistency, with the critique fed back into generation; Qwen-VL-2.5-3B is then used as the objective quiz evaluator in PresentEval. The headline result that PresentAgent 'approaches human-level quality' is therefore measured by a judge whose preferences the generator was explicitly steered toward. No human rating calibration or inter-annotator agreement is provided, so the comparison to human videos is a comparison in the shared Qwen model's own space, not an independent human-level assessment.

full rationale

The paper's derivation chain is modular rather than mathematical, and no fitted parameter is renamed as a prediction. However, the central empirical claim is partially circular: the generator's self-critique model (Qwen-VL-2.5-3B-Instruct) is the same model used for objective quiz evaluation, and the subjective judge (Qwen-Omni-7B) is from the same family as a generation backend (Qwen-VL-Max). This means PresentAgent is optimized toward the evaluator's preferences, so the 'human-level' conclusion is not independent of the measuring instrument. In addition, the abstract's claim of validation on 30 document–presentation pairs is directly contradicted by Section 7, which states 'evaluation was limited to five academic papers, potentially underrepresenting the document diversity shown in our benchmark,' and by Table 3's caption '5-document test set'; this is an evidence mismatch rather than a circularity, but it further weakens the headline. Self-citations in the reference list are extensive but not load-bearing: the core pipeline cites PPTAgent and Paper2Poster, which are not authored by the present team. Score 5 reflects one concrete evaluator/generator overlap that compromises the central claim, without the claim reducing entirely to a fitted parameter or self-citation chain.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claim rests on domain assumptions about evaluation validity rather than on mathematical axioms or invented entities. The most important assumptions are that VLM judges track human perception, that five questions per document are sufficient to measure comprehension, that five academic papers represent the advertised 30-pair benchmark, and that a single human reference video is a valid ground truth. The free-parameter list is short because the paper is a system paper; the main hand-chosen settings are narration duration, quiz size, and the unspecified routing weights.

free parameters (3)
  • Target narration duration = 30 to 150 seconds per slide
    Hand-specified in Section 4.3; controls pacing and affects how much content each slide covers.
  • Quiz size = 5 questions per document
    Fixed in Section 3.2 without sensitivity analysis; the 0-5 score range makes small-sample differences hard to interpret.
  • Dynamic routing policy weights = unspecified
    Section 5.2 says routing weighs input length, complexity, and latency budget but does not report the weights; system outputs depend on this policy.
assumptions (4)
  • domain assumption Qwen-Omni-7B and Qwen-VL-2.5-3B subjective and quiz scores correspond to human viewer judgments
    Sections 3.2 and 5.1 use VLM scores as the evaluation without any human rating study, inter-annotator agreement, or calibration experiment.
  • domain assumption Five multiple-choice questions per document are a reliable measure of audience comprehension
    Section 3.2 fixes five questions; no reliability analysis is provided, and correct answers are annotated from a single human reference video.
  • domain assumption Five academic papers are representative of the 30-pair benchmark spanning papers, web pages, blogs, and slides
    Section 7 limits evaluation to five academic papers, so results cannot generalize to the other advertised document types.
  • domain assumption A single human-authored video is a valid ground-truth reference for each document
    Section 3.1 pairs each document with one human-created presentation video, and quiz answers are based on it; no multiple references or expert agreement is reported.

how reviews work

0 comments
Cite this review

Pith. "Pith review of PresentAgent: Multimodal Agent for Presentation Video Generation." pith.science (2026). https://pith.science/paper/WOJDC4VQ

@misc{pith2026250704036,
  author       = {Pith},
  title        = {Pith review of: PresentAgent: Multimodal Agent for Presentation Video Generation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WOJDC4VQ}},
  note         = {Machine review of arXiv:2507.04036}
}
read the original abstract

We present PresentAgent, a multimodal agent that transforms long-form documents into narrated presentation videos. While existing approaches are limited to generating static slides or text summaries, our method advances beyond these limitations by producing fully synchronized visual and spoken content that closely mimics human-style presentations. To achieve this integration, PresentAgent employs a modular pipeline that systematically segments the input document, plans and renders slide-style visual frames, generates contextual spoken narration with large language models and Text-to-Speech models, and seamlessly composes the final video with precise audio-visual alignment. Given the complexity of evaluating such multimodal outputs, we introduce PresentEval, a unified assessment framework powered by Vision-Language Models that comprehensively scores videos across three critical dimensions: content fidelity, visual clarity, and audience comprehension through prompt-based evaluation. Our experimental validation on a curated dataset of 30 document-presentation pairs demonstrates that PresentAgent approaches human-level quality across all evaluation metrics. These results highlight the significant potential of controllable multimodal agents in transforming static textual materials into dynamic, effective, and accessible presentation formats. Code will be available at https://github.com/AIGeeksGroup/PresentAgent.

Figures

Figures reproduced from arXiv: 2507.04036 by the authors.

Figure 1
Figure 1. Overview of PresentAgent. It takes docu￾ments (e.g., web pages) as input and follows a genera￾tion pipeline: (1) document processing, (2) structured slide generation, (3) synchronized caption creation, and (4) audio synthesis. The final output is a presentation video combining visual slides with aligned narration. The purple-highlighted middle results emphasize the system’s key transitional outputs during generation… view at source ↗
Figure 2
Figure 2. Document Diversity in Our Evaluation Benchmark. [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Overview of our framework. Our approach addresses the full pipeline of document-to-presentation video generation and evaluation. Left: Given diverse input documents—including papers, websites, blogs, slides, and PDFs—PresentAgent generates narrated presentation videos by producing synchronized slide decks with audio. Right: To evaluate these videos, we introduce PresentEval, a two-part evaluation framework: (1) Obje… view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Overview of the PresentAgent framework. Our system takes diverse documents (e.g., papers, websites, PDFs) as input and follows a modular generation pipeline. It first performs outline generation (Step 1) and retrieves the most suitable template (Step 2), then generates…
Figure 5
Figure 5. Figure 5: PresentAgent Demo. Automatically generates academic-style slides and narrated videos from research papers, streamlining the transformation from written content to engaging visual presentations. primary LLM back ends in parallel—GPT-4o, GPT-4o-mini, Qwen-VL-Max, Gemini-…

Discussion (0). Sign in to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. OmniPresent: Generating Coherent Presentation Suites from Scientific Papers

    cs.SE 2026-07 conditional novelty 6.5 of 10

    A multi-agent HTML pipeline with shared knowledge and cross-artifact verify-and-repair generates coherent poster/slides/video/page suites from papers and beats specialized baselines on OmniPreBench.

  2. BIFE: Better Interaction, Fewer Errors for Minute-Long Video Generation

    cs.CV 2025-11 conditional novelty 5.0 of 10

    BlockVid generates minute-long videos with a semantic sparse KV cache, Block Forcing training, and chunk-level noise scheduling, reporting large gains on its own LV-Bench and on VBench.

Reference graph

Works this paper leans on

55 extracted references · 15 canonical work pages · cited by 2 Pith papers

  1. [1]

    Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, and 1 others. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774

    arXiv 2023

  2. [2]

    Anthropic. 2024. Claude 3 technical overview. https://www.anthropic.com/news/claude-3. Accessed: 2025-06-30

    work page 2024

  3. [3]

    Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, and 1 others. 2025. Qwen2. 5-vl technical report. arXiv preprint arXiv:2502.13923

    arXiv 2025

  4. [4]

    Iz Beltagy, Matthew E Peters, and Arman Cohan. 2020. Longformer: The long-document transformer. arXiv preprint arXiv:2004.05150

    arXiv 2020

  5. [5]

    Jiaao Chen and Diyi Yang. 2021. Structure-aware abstractive conversation summarization via discourse and action graphs. arXiv preprint arXiv:2104.08400

    work page Pith review arXiv 2021

  6. [6]

    Google DeepMind. 2024. Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities. https://deepmind.google/technologies/gemini/. Accessed: 2025-06-30

    work page 2024

  7. [7]

    Chaorui Deng, Deyao Zhu, Kunchang Li, Chenhui Gou, Feng Li, Zeyu Wang, Shu Zhong, Weihao Yu, Xiaonan Nie, Ziang Song, and 1 others. 2025. Emerging properties in unified multimodal pretraining. arXiv preprint arXiv:2505.14683

    arXiv 2025

  8. [8]

    Tsu-Jui Fu, William Yang Wang, Daniel McDuff, and Yale Song. 2022. Doc2ppt: Automatic presentation slides generation from scientific documents. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 634--642

    work page 2022

Show all 55 references
  1. [9]

    Jiaxin Ge, Zora Zhiruo Wang, Xuhui Zhou, Yi-Hao Peng, Sanjay Subramanian, Qinyue Tan, Maarten Sap, Alane Suhr, Daniel Fried, Graham Neubig, and Trevor Darrell. 2025. https://arxiv.org/abs/2501.00912 Autopresent: Designing structured visuals from scratch . arXiv preprint arXiv:...

    2025 arXiv

  2. [10]

    Yingqing He, Menghan Xia, Haoxin Chen, Xiaodong Cun, Yuan Gong, Jinbo Xing, Yong Zhang, Xintao Wang, Chao Weng, Ying Shan, and 1 others. 2023. Animate-a-story: Storytelling with retrieval-augmented video generation. arXiv preprint arXiv:2307.06940

    2023 arXiv

  3. [11]

    Levon Khachatryan, Andranik Movsisyan, Vahram Tadevosyan, Roberto Henschel, Zhangyang Wang, Shant Navasardyan, and Humphrey Shi. 2023. Text2video-zero: Text-to-image diffusion models are zero-shot video generators. In Proceedings of the IEEE/CVF International Conference on Com...

    2023

  4. [12]

    Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019. Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461

    2019 arXiv

  5. [13]

    Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Peiyuan Zhang, Yanwei Li, Ziwei Liu, and 1 others. 2024. Llava-onevision: Easy visual task transfer. arXiv preprint arXiv:2408.03326

    2024 arXiv

  6. [14]

    Xin Li, Wenqing Chu, Ye Wu, Weihang Yuan, Fanglong Liu, Qi Zhang, Fu Li, Haocheng Feng, Errui Ding, and Jingdong Wang. 2023. Videogen: A reference-guided latent diffusion approach for high definition text-to-video generation. arXiv preprint arXiv:2309.00398

    2023 arXiv

  7. [15]

    Kevin Qinghong Lin, Linjie Li, Difei Gao, Qinchen Wu, Mingyi Yan, Zhengyuan Yang, Lijuan Wang, and Mike Zheng Shou. 2024 a . Videogui: A benchmark for gui automation from instructional videos. arXiv preprint arXiv:2406.10227

    2024 arXiv

  8. [16]

    Kevin Qinghong Lin, Linjie Li, Difei Gao, Zhengyuan Yang, Shiwei Wu, Zechen Bai, Weixian Lei, Lijuan Wang, and Mike Zheng Shou. 2024 b . Showui: One vision-language-action model for gui visual agent. arXiv preprint arXiv:2411.17465

    2024 arXiv

  9. [17]

    Akide Liu, Zeyu Zhang, Zhexin Li, Xuehai Bai, Yizeng Han, Jiasheng Tang, Yuanjie Xing, Jichao Wu, Mingyang Yang, Weihua Chen, and 1 others. 2025. Fpsattention: Training-aware fp8 and sparsity co-design for fast video diffusion. arXiv preprint arXiv:2506.04648

    2025 arXiv

  10. [18]

    Pan Lu, Bowen Chen, Sheng Liu, Rahul Thapa, Joseph Boen, and James Zou. 2025. Octotools: An agentic framework with extensible tools for complex reasoning. arXiv preprint arXiv:2502.11271

    2025 arXiv

  11. [19]

    Rodriguez, Montek Kalsi, Rabiul Awal, Nicolas Chapados, M

    Shravan Nayak, Xiangru Jian, Kevin Qinghong Lin, Juan A. Rodriguez, Montek Kalsi, Rabiul Awal, Nicolas Chapados, M. Tamer Özsu, Aishwarya Agrawal, David Vazquez, Christopher Pal, Perouz Taslakian, Spandana Gella, and Sai Rajeswar. 2025. Ui-vision: A desktop-centric gui benchma...

    2025 arXiv

  12. [20]

    Junrui Ni, Liming Wang, Heting Gao, Kaizhi Qian, Yang Zhang, Shiyu Chang, and Mark Hasegawa-Johnson. 2022. Unsupervised text-to-speech synthesis by unsupervised automatic speech recognition. arXiv preprint arXiv:2203.15796

    2022 arXiv

  13. [21]

    Wei Pang, Kevin Qinghong Lin, Xiangru Jian, Xi He, and Philip Torr. 2025. Paper2poster: Towards multimodal poster automation from scientific papers. arXiv preprint arXiv:2505.21497

    2025

  14. [22]

    Vadim Popov, Ivan Vovk, Vladimir Gogoryan, Tasnima Sadekova, and Mikhail Kudinov. 2021. Grad-tts: A diffusion probabilistic model for text-to-speech. In International conference on machine learning, pages 8599--8608. PMLR

    2021

  15. [23]

    Yujia Qin, Yining Ye, Junjie Fang, Haoming Wang, Shihao Liang, Shizuo Tian, Junda Zhang, Jiahao Li, Yunxin Li, Shijue Huang, and 1 others. 2025. Ui-tars: Pioneering automated gui interaction with native agents. arXiv preprint arXiv:2501.12326

    2025 arXiv

  16. [24]

    Yi Ren, Yangjun Ruan, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, and Tie-Yan Liu. 2019. Fastspeech: Fast, robust and controllable text to speech. Advances in neural information processing systems, 32

    2019

  17. [25]

    Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, and et al. 2023. Toolformer: Language models can teach themselves to use tools. arXiv preprint arXiv:2302.04761

    2023 arXiv

  18. [26]

    Shivam R Solanki and Drupad K Khublani. 2024. From script to screen: Unveiling text-to-video generation. In Generative Artificial Intelligence: Exploring the Power and Potential of Generative AI, pages 81--112. Springer

    2024

  19. [27]

    Zirui Song, Qian Jiang, Mingxuan Cui, Mingzhe Li, Lang Gao, Zeyu Zhang, Zixiang Xu, Yanbo Wang, Chenxi Wang, Guangxian Ouyang, and 1 others. 2025 a . Audio jailbreak: An open comprehensive benchmark for jailbreaking large audio-language models. arXiv preprint arXiv:2505.15406

    2025 arXiv

  20. [28]

    Zirui Song, Guangxian Ouyang, Meng Fang, Hongbin Na, Zijing Shi, Zhenhao Chen, Yujie Fu, Zeyu Zhang, Shiyu Jiang, Miao Fang, and 1 others. 2024. Hazards in daily life? enabling robots to proactively detect and resolve anomalies. arXiv preprint arXiv:2411.00781

    2024 arXiv

  21. [29]

    Zirui Song, Guangxian Ouyang, Mingzhe Li, Yuheng Ji, Chenxi Wang, Zixiang Xu, Zeyu Zhang, Xiaoqing Zhang, Qian Jiang, Zhenhao Chen, and 1 others. 2025 b . Maniplvm-r1: Reinforcement learning for reasoning in embodied manipulation with large vision-language models. arXiv prepri...

    2025 arXiv

  22. [30]

    Zirui Song, Jingpu Yang, Yuan Huang, Jonathan Tonglet, Zeyu Zhang, Tao Cheng, Meng Fang, Iryna Gurevych, and Xiuying Chen. 2025 c . Geolocation with real human gameplay data: A large-scale dataset and human-like reasoning framework. arXiv preprint arXiv:2502.13759

    2025

  23. [31]

    Qiushi Sun, Kanzhi Cheng, Zichen Ding, Chuanyang Jin, Yian Wang, Fangzhi Xu, Zhenyu Wu, Chengyou Jia, Liheng Chen, Zhoumianze Liu, and 1 others. 2024. Os-genesis: Automating gui agent trajectory construction via reverse task synthesis. arXiv preprint arXiv:2412.19723

    2024 arXiv

  24. [32]

    Hideyuki Tachibana, Katsuya Uenoyama, and Shunsuke Aihara. 2018. Efficiently trainable text-to-speech system based on deep convolutional networks with guided attention. In 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP), pages 4784--4788. IEEE

    2018

  25. [33]

    Baode Wang, Biao Wu, Weizhen Li, Meng Fang, Yanjie Liang, Zuming Huang, Haozhe Wang, Jun Huang, Ling Chen, Wei Chu, and 1 others. 2025. Infinity parser: Layout aware reinforcement learning for scanned document parsing. arXiv preprint arXiv:2506.03197

    2025

  26. [34]

    Guanghua Wang, Priyanshi Garg, and Weili Wu. 2024 a . Segmented summarization and refinement: A pipeline for long-document analysis on social media. Journal of Social Computing, 5(2):132--144

    2024

  27. [35]

    Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, and 1 others. 2024 b . Qwen2-vl: Enhancing vision-language model's perception of the world at any resolution. arXiv preprint arXiv:2409.12191

    2024 arXiv

  28. [36]

    Xingyao Wang, Boxuan Li, Yufan Song, Frank F Xu, Xiangru Tang, Mingchen Zhuge, Jiayi Pan, Yueqi Song, Bowen Li, Jaskirat Singh, and 1 others. 2024 c . Opendevin: An open platform for ai software developers as generalist agents. arXiv preprint arXiv:2407.16741

    2024 arXiv

  29. [37]

    Biao Wu, Yanda Li, Meng Fang, Zirui Song, Zhiwei Zhang, Yunchao Wei, and Ling Chen. 2024. Foundations and recent trends in multimodal mobile agents: A survey. arXiv preprint arXiv:2411.02006

    2024 arXiv

  30. [38]

    Jinheng Xie, Weijia Mao, Zechen Bai, David Junhao Zhang, Weihao Wang, Kevin Qinghong Lin, Yuchao Gu, Zhijie Chen, Zhenheng Yang, and Mike Zheng Shou. 2024. Show-o: One single transformer to unify multimodal understanding and generation. arXiv preprint arXiv:2408.12528

    2024 arXiv

  31. [39]

    Jin Xu, Zhifang Guo, Jinzheng He, Hangrui Hu, Ting He, Shuai Bai, Keqin Chen, Jialin Wang, Yang Fan, Kai Dang, and 1 others. 2025. Qwen2. 5-omni technical report. arXiv preprint arXiv:2503.20215

    2025 arXiv

  32. [40]

    Qiyao Xue, Xiangyu Yin, Boyuan Yang, and Wei Gao. 2025. Phyt2v: Llm-guided iterative self-refinement for physics-grounded text-to-video generation. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 18826--18836

    2025

  33. [41]

    John Yang, Carlos Jimenez, Alexander Wettig, Kilian Lieret, Shunyu Yao, Karthik Narasimhan, and Ofir Press. 2024 a . Swe-agent: Agent-computer interfaces enable automated software engineering. Advances in Neural Information Processing Systems, 37:50528--50652

    2024

  34. [42]

    Ke Yang, Jiateng Liu, John Wu, Chaoqi Yang, Yi R Fung, Sha Li, Zixuan Huang, Xu Cao, Xingyao Wang, Yiquan Wang, and 1 others. 2024 b . If llm is the wizard, then code is the wand: A survey on how code empowers large language models to serve as intelligent agents. arXiv preprin...

    2024 arXiv

  35. [43]

    Rui Yang, Lin Song, Yanwei Li, Sijie Zhao, Yixiao Ge, Xiu Li, and Ying Shan. 2023 a . Gpt4tools: Teaching large language model to use tools via self-instruction. Advances in Neural Information Processing Systems, 36:71995--72007

    2023

  36. [44]

    Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Ehsan Azarnasab, Faisal Ahmed, Zicheng Liu, Ce Liu, Michael Zeng, and Lijuan Wang. 2023 b . Mm-react: Prompting chatgpt for multimodal reasoning and action. arXiv preprint arXiv:2303.11381

    2023 arXiv

  37. [45]

    Zhuoyi Yang, Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, Wenyi Hong, Xiaohan Zhang, Guanyu Feng, and 1 others. 2024 c . Cogvideox: Text-to-video diffusion models with an expert transformer. arXiv preprint arXiv:2408.06072

    2024 arXiv

  38. [46]

    Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik R Narasimhan, and Yuan Cao. 2023. https://openreview.net/forum?id=WE_vluYUL-X React: Synergizing reasoning and acting in language models . In The Eleventh International Conference on Learning Representations

    2023

  39. [47]

    Murong Yue, Wenlin Yao, Haitao Mi, Dian Yu, Ziyu Yao, and Dong Yu. 2024. Dots: Learning to reason dynamically in llms via optimal reasoning trajectories search. arXiv preprint arXiv:2410.03864

    2024 arXiv

  40. [48]

    Zeyu Zhang, Hang Gao, Akide Liu, Qi Chen, Feng Chen, Yiran Wang, Danning Li, Rui Zhao, Zhenming Li, Zhongwen Zhou, and 1 others. 2024 a . Kmm: Key frame mask mamba for extended motion generation. arXiv preprint arXiv:2411.06481

    2024 arXiv

  41. [49]

    Zeyu Zhang, Akide Liu, Qi Chen, Feng Chen, Ian Reid, Richard Hartley, Bohan Zhuang, and Hao Tang. 2024 b . Infinimotion: Mamba boosts memory in transformer for arbitrary long motion generation. arXiv preprint arXiv:2407.10061

    2024 arXiv

  42. [50]

    Zeyu Zhang, Akide Liu, Ian Reid, Richard Hartley, Bohan Zhuang, and Hao Tang. 2024 c . Motion mamba: Efficient and long sequence motion generation. In European Conference on Computer Vision, pages 265--282. Springer

    2024

  43. [51]

    Zeyu Zhang, Yiran Wang, Wei Mao, Danning Li, Rui Zhao, Biao Wu, Zirui Song, Bohan Zhuang, Ian Reid, and Richard Hartley. 2025. Motion anything: Any to motion generation. arXiv preprint arXiv:2503.06955

    2025 arXiv

  44. [52]

    Zeyu Zhang, Yiran Wang, Biao Wu, Shuo Chen, Zhiyuan Zhang, Shiya Huang, Wenbo Zhang, Meng Fang, Ling Chen, and Yang Zhao. 2024 d . Motion avatar: Generate human and animal avatars with arbitrary motion. arXiv preprint arXiv:2405.11286

    2024 arXiv

  45. [54]

    Hao Zheng, Xinyan Guan, Hao Kong, Jia Zheng, Weixiang Zhou, Hongyu Lin, Yaojie Lu, Ben He, Xianpei Han, and Le Sun. 2025 b . Pptagent: Generating and evaluating presentations beyond text-to-slides. arXiv preprint arXiv:2501.03936

    2025 arXiv

  46. [55]

    online" 'onlinestring :=

    ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list...

  47. [56]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.