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REVIEW 4 major objections 5 minor 3 cited by

Ming-Lite-Uni: Advancements in Unified Architecture for Natural Multimodal Interaction

T0 review · 4 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read Ming-Lite-Uni shows that a frozen multimodal LLM plus a trainable diffusion model can read, draw, and edit images in one system.

desk verdict Open-sourced alpha model with plausible architecture but unverified headline gains and self-contradictory benchmark claims. read the letter →

arxiv 2505.02471 v3 pith:6XE4ZQL5 submitted 2025-05-05 cs.CV

Inclusion AI , Biao Gong , Cheng Zou , Dandan Zheng , Hu Yu , Jingdong Chen , Jianxin Sun , Junbo Zhao
show 9 more authors
Jun Zhou Kaixiang Ji Lixiang Ru Libin Wang Qingpei Guo Rui Liu Weilong Chai Xinyu Xiao Ziyuan Huang
This is my paper · ORCID
classification cs.CV
keywords Ming-Lite-Uniunifiedmultimodalarchitecturemulti-scalelearnabletokensrepresentationalignmenttext-to-imagegenerationinstruction-basedimageeditingdiffusionmodelautoregressive
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

This paper introduces Ming-Lite-Uni, an open-source system that pairs a frozen (immutable) multimodal large language model with a learnable diffusion model. The central claim is that a native autoregressive model, which normally only understands images and language, can also generate images from text and follow editing instructions when a separate diffusion backbone is fine-tuned behind it. The bridge is a set of multi-scale learnable tokens and a multi-scale representation alignment loss, which let the frozen model pass semantic context to the diffusion model while preserving pixel-level detail. On the GenEval benchmark for text-to-image alignment the system scores 0.62, and on seven understanding benchmarks it averages 69.7, which the paper presents as evidence that adding generation does not sacrifice understanding. The paper also offers qualitative demonstrations of instruction-based image editing and style transfer, and it releases code and weights in an alpha state.

What carries the argument

The load-bearing mechanism is the multi-scale learnable query token set $Q=\{Q_{s_1},Q_{s_2},Q_{s_3}\}$, defined at resolutions $4\times4$, $8\times8$, and $16\times16$; each scale gets its own learned start and end markers and positional grid encodings before a transformer encoder produces hidden representations $H=f_\theta(Z_{\text{input}})$. These hidden states condition a diffusion transformer that denoises under a flow-matching objective, while a multi-scale representation alignment loss minimizes the mean squared error between intermediate diffusion hidden states and the final semantic representations. That two-part machinery is what lets the frozen autoregressive language model supply contextual understanding while the trainable diffusion model supplies high-fidelity synthesis.

What would settle it

A controlled ablation that trains the same system twice, once with and once without the multi-scale representation alignment loss while holding all data and hyperparameters fixed, would settle the central claim; if the reported PSNR and text-to-image benchmark gains disappear when the loss is removed, the alignment mechanism is doing the work, and if they do not, the paper's novel contribution has no demonstrated effect.

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Extended reading notes

Core claim

Ming-Lite-Uni's central discovery is that the visual-generation gap in native autoregressive multimodal models can be closed by keeping the language model fixed and fine-tuning only a diffusion model, joined through multi-scale learnable query tokens. The image is represented at three scales—low, mid, and high resolution—with explicit boundary markers and positional encodings so the generator sees layout, objects, and texture simultaneously. A scale-wise consistency loss then aligns intermediate hidden states of the diffusion transformer with its final semantic output; the paper reports this raises reconstruction quality by more than 2 dB PSNR and improves the text-to-image benchmark score by 1.5%. The resulting model achieves a 0.62 overall score on GenEval while averaging 69.7 on seven understanding benchmarks, and the paper's qualitative results show multi-step instruction editing and style transfer working in the same dialogue flow.

Load-bearing premise

The claim that the multi-scale representation alignment loss is the active ingredient in the improved generation quality is asserted with numbers (over 2 dB PSNR and a 1.5 percent GenEval gain) but is not supported by any ablation or experiment shown in the paper.

Editorial extensions

If this is right

  • A frozen multimodal LLM can acquire image generation and editing capability by training only the diffusion side, so the expensive language backbone does not need to be retrained for new visual abilities.
  • The system's 0.62 score on GenEval places it at parity with dedicated generation models while its 69.7 average across seven understanding benchmarks remains competitive, indicating the two capabilities can coexist.
  • Because the editing portion of the training data contains multi-turn edit sequences, the model can follow chains of instructions that modify an image repeatedly in one conversation.
  • Releasing the code and model weights in alpha form means other teams can adopt the multi-scale token bridge immediately, with a fuller evaluation promised for the next release.

Reading between the lines

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

  • If the multi-scale representation alignment loss is truly responsible for the reported gains, the same alignment recipe should transfer to any frozen-LLM-plus-diffusion system; the cheapest test is an ablation that toggles only that loss.
  • Because the language model is frozen, the approach decouples the two sides of the model: users could swap in a better diffusion generator or a better LLM without retraining the other half.
  • The multi-scale token scheme is defined by a set of grid resolutions rather than by image-specific structure, so it could in principle be extended to video or audio output tokens, although the paper does not claim this.
  • The reported 2 dB PSNR and 1.5 percent benchmark improvements are stated without a displayed ablation; until the promised full evaluation appears, those numbers are best read as preliminary.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper introduces Ming-Lite-Uni, an open-source framework that couples a frozen multimodal LLM (M2-omni, based on Llama-3) with a learnable SANA-style diffusion model through two proposed mechanisms: multi-scale learnable query tokens with explicit scale boundary markers, and a multi-scale representation alignment loss that aligns intermediate DiT hidden states with final semantic representations. The stated goal is to equip native multimodal autoregressive models with text-to-image generation and instruction-based image editing capabilities while preserving understanding. The manuscript reports quantitative results on seven multimodal understanding benchmarks and on GenEval for text-to-image generation, plus qualitative editing examples. The paper is explicitly in alpha stage and states that a full experimental evaluation will follow in a future release.

Significance. If substantiated, the core idea of using multi-scale learnable tokens and aligning intermediate diffusion hidden states with semantic representations inside a frozen-MLLM/learnable-diffusion hybrid would be a useful contribution to unified multimodal architectures, and the open-source release is a community asset. However, the paper currently provides no experimental evidence that the novel components are responsible for the reported performance: the quantitative claims for the multi-scale alignment are asserted without ablation, and several headline statements are contradicted by the paper's own tables. The significance as a research contribution is therefore not yet established; the paper reads as a preliminary technical report rather than a complete experimental study.

major comments (4)
  1. [Sec. 2.1] The multi-scale representation alignment is described as a 'scale wised consistency loss' that 'directly enhances the high-res reconstruction quality (>2dB PSNR) and boosts GenEval by 1.5%.' No equation for the loss is given, and no ablation, PSNR measurement, or GenEval comparison with and without the loss appears anywhere in the paper. Because this alignment is one of the two claimed novel contributions, the paper's central improvement claim is unsupported. The authors should either supply the missing experiments or remove the quantitative assertions and state explicitly that the effect of the alignment has not yet been measured.
  2. [Sec. 4.1] The text claims that 'Our model achieves top-tier performance on most benchmarks, surpassing closed-source models like GPT-4o and Gemini-1.5-Pro.' Table 2 shows, however, that Ming-Lite-Uni's average is 69.7 versus GPT-4o's 72.0, and that the model exceeds GPT-4o on only MathV (68.3 vs. 59.9) while trailing on MMB, MMS, MMMU, Hall, AI2D, and MM-Vet. This statement is directly contradicted by the paper's own results and must be corrected.
  3. [Sec. 4.2] The text states that 'our Ming-Lite-Uni obtains 0.62 overall accuracy on GenEval, which outperforms all the other unified or generation-only methods, e.g., MetaQueries (0.61), DALL-E 3 (0.67).' Table 3 lists DALL-E 3 at 0.67 and SD3-Medium at 0.74, both above 0.62, and Janus-Pro-1B at 0.73. The claim of outperforming all other methods is therefore false. The subsequent sentence about matching state-of-the-art performance on single-subject images is more defensible, but the overall statement needs revision.
  4. [Abstract and Sec. 1] The manuscript repeatedly acknowledges missing evidence: the abstract states that 'Ming-Lite-Uni is in alpha stage and will soon be further refined,' and Sec. 1 concludes with 'All code and weights have been open-sourced, with a full experimental evaluation to follow in the next release.' These admissions indicate that the experimental validation is incomplete at submission time. Since the central claims depend on experiments that are not reported, the manuscript in its present form does not meet the standard of a complete research paper; it is more a technical report or project announcement.
minor comments (5)
  1. [Sec. 3.1] There is a typo: 'Our tarining dataset' should be 'Our training dataset.'
  2. [Sec. 2.1] The multi-scale alignment loss is described verbally as minimizing mean squared error, but no formal loss function is provided. Please give the explicit equation and specify exactly which intermediate hidden states are aligned with which final representations.
  3. [Table 3] The method 'TokenFlow-XL' is attributed to Liu et al. (2024), but the reference for Liu et al. (2024) in the bibliography is 'World model on million-length video and language with ringattention,' which is not the TokenFlow paper. Either the citation is wrong or the reference is missing; please correct this.
  4. [Table 2] Several model names are misformatted, e.g., 'LLaV A-72B' and 'Y ue' in references. A careful proofreading pass is needed.
  5. [Sec. 1] The paper states 'All code and model weights are open-sourced,' but only a GitHub code link is given; please provide the model weight release link or specify where the weights can be obtained.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the reported gains are benchmarked externally; the unsupported alignment-loss claim is an evidence gap, not a definitional reduction.

full rationale

Ming-Lite-Uni is an empirical systems report with no derivation chain in which an output is shown to equal an input by construction. The central generation claim is evaluated on GenEval (Table 3) and understanding on OpenCompass (Table 2), both external benchmarks. The model fixes an MLLM (M2-omni) and fine-tunes a diffusion backbone; performance numbers are measured, not derived from the loss definition. Self-references to Guo et al. (2025) and Pan et al. (2025) are component reuse and are not used to substitute for measurement: M2-omni's pretraining is cited for details, while the benchmark numbers for Ming-Lite-Uni are newly reported. Section 2.1 asserts without an ablation that multi-scale representation alignment 'directly enhances the high-res reconstruction quality (>2dB PSNR) and boosts GenEval by 1.5%,' but this is an unsupported causal claim about an ablation, not a case where a fitted parameter is renamed a prediction or where an equation reduces to itself. Likewise, the incorrect comparative statements in Sections 4.1 and 4.2 (claiming to surpass GPT-4o and DALL-E 3 while the tables show lower scores) are internal consistency errors, not circularity. No step in the paper exhibits the required reduction of Eq. X = Eq. Y by construction or a self-citation chain forcing the result, so the circularity score is 0.

Assumptions & free parameters 2 free parameters · 4 assumptions · 3 invented entities

The central engineering claim rests on reusing the authors' M2-omni pretraining, the MetaQueries bridging framework, and SANA as the diffusion backbone. The genuinely new components (multi-scale tokens, alignment loss) are introduced without ablations or independent evidence, so the reader must accept the reported aggregate benchmark results as evidence for their contribution.

free parameters (2)
  • Multi-scale token resolutions S = {4x4, 8x8, 16x16}
    The three resolution scales are hand-chosen in Sec 2.1, and no ablation justifies the choice.
  • Representation alignment loss weight = not stated in paper
    The MSE alignment term in Sec 2.1 has no stated loss weight or schedule, yet the claimed improvement depends on it.
assumptions (4)
  • domain assumption The M2-omni pretraining procedure and dataset are valid for the AR backbone
    Sec 2.2 reuses M2-omni pretraining without further validation, citing Guo et al. (2025).
  • domain assumption SANA can serve as the diffusion generator when fine-tuned with flow matching loss
    Sec 2 states the generator is an externally trainable diffusion model (e.g., SANA) with no independent evaluation of this base choice.
  • domain assumption The MetaQueries bridging framework transfers semantics between modalities
    Sec 1 says Ming-Lite-Uni is an implementation of the integrated MetaQueries and M2-omni framework, so the bridge is assumed to work.
  • standard math Standard transformer and diffusion mathematics are taken as given
    The architecture relies on well-known transformer, flow matching, and diffusion formulations without deriving them.
invented entities (3)
  • Multi-scale learnable query tokens
    purpose: Carry visual information at three resolutions into the frozen MLLM
    No ablation isolates their contribution; benchmark results are aggregate, so there is no falsifiable handle outside this paper.
  • Multi-scale representation alignment loss
    purpose: Align intermediate DiT hidden states with final semantic representations to improve generation
    The claimed >2dB PSNR and +1.5% GenEval gains are stated without supporting experiments in Sec 2.1.
  • Scale boundary markers (START_sk, END_sk)
    purpose: Preserve scale-specific semantics in token sequences
    Introduced in Sec 2.1 with no separate validation.

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Cite this review

Pith. "Pith review of Ming-Lite-Uni: Advancements in Unified Architecture for Natural Multimodal Interaction." pith.science (2026). https://pith.science/paper/6XE4ZQL5

@misc{pith2026250502471,
  author       = {Pith},
  title        = {Pith review of: Ming-Lite-Uni: Advancements in Unified Architecture for Natural Multimodal Interaction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6XE4ZQL5}},
  note         = {Machine review of arXiv:2505.02471}
}
read the original abstract

We introduce Ming-Lite-Uni, an open-source multimodal framework featuring a newly designed unified visual generator and a native multimodal autoregressive model tailored for unifying vision and language. Specifically, this project provides an open-source implementation of the integrated MetaQueries and M2-omni framework, while introducing the novel multi-scale learnable tokens and multi-scale representation alignment strategy. By leveraging a fixed MLLM and a learnable diffusion model, Ming-Lite-Uni enables native multimodal AR models to perform both text-to-image generation and instruction based image editing tasks, expanding their capabilities beyond pure visual understanding. Our experimental results demonstrate the strong performance of Ming-Lite-Uni and illustrate the impressive fluid nature of its interactive process. All code and model weights are open-sourced to foster further exploration within the community. Notably, this work aligns with concurrent multimodal AI milestones - such as ChatGPT-4o with native image generation updated in March 25, 2025 - underscoring the broader significance of unified models like Ming-Lite-Uni on the path toward AGI. Ming-Lite-Uni is in alpha stage and will soon be further refined.

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Forward citations

Cited by 3 Pith papers

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

  1. Ming-Omni: A Unified Multimodal Model for Perception and Generation

    cs.AI 2025-06 conditional novelty 6.0 of 10

    A single model with modality-specific routing processes image, text, audio, and video inputs and generates text, speech, and images, with public benchmarks reported across all of these abilities.

  2. TokLIP: Marry Visual Tokens to CLIP for Multimodal Comprehension and Generation

    cs.CV 2025-05 conditional novelty 6.0 of 10

    TokLIP semanticizes VQ image tokens with a causal CLIP-style encoder, improving multimodal comprehension while preserving autoregressive image generation.

  3. Ovis-U1 Technical Report

    cs.CV 2025-06 conditional novelty 5.0 of 10

    A 3B unified multimodal model with a diffusion decoder and bidirectional refiner achieves competitive understanding, generation, and editing benchmark scores.

Reference graph

Works this paper leans on

29 extracted references · 27 linked inside Pith · cited by 3 Pith papers

  1. [1]

    Qwen-vl: A frontier large vision-language model with versatile abilities.arXiv preprint arXiv:2308.12966,

    Jinze Bai, Shuai Bai, Shusheng Y ang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. Qwen-vl: A frontier large vision-language model with versatile abilities.arXiv preprint arXiv:2308.12966,

    arXiv

  2. [4]

    Are we on the right way for evaluating large vision-language models?arXiv preprint arXiv:2403.20330, 2024a

    Lin Chen, Jinsong Li, Xiaoyi Dong, Pan Zhang, Y uhang Zang, Zehui Chen, Haodong Duan, Jiaqi Wang, Y u Qiao, Dahua Lin, et al. Are we on the right way for evaluating large vision-language models?arXiv preprint arXiv:2403.20330, 2024a. Xiaokang Chen, Zhiyu Wu, Xingchao Liu, Zizheng Pan, Wen Liu, Zhenda Xie, Xingkai Y u, and Chong Ruan. Janus-pro: Unified mu...

    arXiv

  3. [7]

    The llama 3 herd of models.arXiv preprint arXiv:2407.21783,

    Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Y ang, Angela Fan, et al. The llama 3 herd of models.arXiv preprint arXiv:2407.21783,

    arXiv

  4. [8]

    Scaling rectified flow transformers for high-resolution image synthesis, 2024.URL https://arxiv

    Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Y am Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling rectified flow transformers for high-resolution image synthesis, 2024.URL https://arxiv. org/abs/2403.03206, 2, 2024a. Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller...

    arXiv 2024

  5. [9]

    Seed-data-edit technical report: A hybrid dataset for instructional image editing.arXiv preprint arXiv:2405.04007, 2024a

    Y uying Ge, Sijie Zhao, Chen Li, Yixiao Ge, and Ying Shan. Seed-data-edit technical report: A hybrid dataset for instructional image editing.arXiv preprint arXiv:2405.04007, 2024a. Y uying Ge, Sijie Zhao, Jinguo Zhu, Yixiao Ge, Kun Yi, Lin Song, Chen Li, Xiaohan Ding, and Ying Shan. Seed-x: Multimodal models with unified multi-granularity comprehension an...

    arXiv

  6. [10]

    M2-omni: Advancing omni-mllm for comprehensive modality support with competitive performance.arXiv preprint arXiv:2502.18778,

    Qingpei Guo, Kaiyou Song, Zipeng Feng, Ziping Ma, Qinglong Zhang, Sirui Gao, Xuzheng Y u, Y unxiao Sun, Tai-Wei Chang, Jingdong Chen, et al. M2-omni: Advancing omni-mllm for comprehensive modality support with competitive performance.arXiv preprint arXiv:2502.18778,

    arXiv

  7. [11]

    Stylebooth: Image style editing with multimodal instruction

    Zhen Han, Chaojie Mao, Zeyinzi Jiang, Y ulin Pan, and Jingfeng Zhang. Stylebooth: Image style editing with multimodal instruction. arXiv preprint arXiv:2404.12154,

    arXiv

  8. [12]

    Learning disentangled identifiers for action-customized text-to-image generation

    Siteng Huang, Biao Gong, Y utong Feng, Xi Chen, Y uqian Fu, Y u Liu, and Donglin Wang. Learning disentangled identifiers for action-customized text-to-image generation. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7797–7806, 2024a. Yipo Huang, Xiangfei Sheng, Zhichao Y ang, Quan Y uan, Zhichao Duan, Pengfei Ch...

    arXiv

Show all 29 references
  1. [13]

    Apddv2: Aesthetics of paintings and drawings dataset with artist labeled scores and comments, 2024.https://arxiv.org/abs/2411.08545

    Xin Jin, Qianqian Qiao, Yi Lu, Huaye Wang, Heng Huang, Shan Gao, Jianfei Liu, and Rui Li. Apddv2: Aesthetics of paintings and drawings dataset with artist labeled scores and comments, 2024.https://arxiv.org/abs/2411.08545. Aniruddha Kembhavi, Mike Salvato, Eric Kolve, Minjoon ...

    2024 arXiv

  2. [15]

    World model on million-length video and language with ringattention.arXiv preprint arXiv:2402.08268,

    Hao Liu, Wilson Y an, Matei Zaharia, and Pieter Abbeel. World model on million-length video and language with ringattention.arXiv preprint arXiv:2402.08268,

    arXiv

  3. [16]

    Deepseek-vl: towards real-world vision-language understanding.arXiv preprint arXiv:2403.05525, 2024a

    Haoyu Lu, Wen Liu, Bo Zhang, Bingxuan Wang, Kai Dong, Bo Liu, Jingxiang Sun, Tongzheng Ren, Zhuoshu Li, Y aofeng Sun, et al. Deepseek-vl: towards real-world vision-language understanding.arXiv preprint arXiv:2403.05525, 2024a. Pan Lu, Hritik Bansal, Tony Xia, Jiacheng Liu, Chu...

    arXiv

  4. [18]

    Transfer between modalities with metaqueries.arXiv preprint arXiv:2504.06256,

    Xichen Pan, Satya Narayan Shukla, Aashu Singh, Zhuokai Zhao, Shlok Kumar Mishra, Jialiang Wang, Zhiyang Xu, Jiuhai Chen, Kunpeng Li, Felix Juefei-Xu, et al. Transfer between modalities with metaqueries.arXiv preprint arXiv:2504.06256,

    arXiv

  5. [19]

    Sdxl: Improving latent diffusion models for high-resolution image synthesis.arXiv preprint arXiv:2307.01952,

    Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. Sdxl: Improving latent diffusion models for high-resolution image synthesis.arXiv preprint arXiv:2307.01952,

    arXiv

  6. [20]

    Tokenflow: Unified image tokenizer for multimodal understanding and generation.arXiv preprint arXiv:2412.03069,

    Liao Qu, Huichao Zhang, Yiheng Liu, Xu Wang, Yi Jiang, Yiming Gao, Hu Y e, Daniel K Du, Zehuan Y uan, and Xinglong Wu. Tokenflow: Unified image tokenizer for multimodal understanding and generation.arXiv preprint arXiv:2412.03069,

    arXiv

  7. [21]

    Hierarchical text-conditional image generation with clip latents.arXiv preprint arXiv:2204.06125, 1(2):3,

    Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text-conditional image generation with clip latents.arXiv preprint arXiv:2204.06125, 1(2):3,

    arXiv

  8. [22]

    Laion-5b: An open large-scale dataset for training next generation image-text models.arXiv preprint arXiv:2210.08402,

    Christoph Schuhmann, Romain Beaumont, Richard V encu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al. Laion-5b: An open large-scale dataset for training next generation image-text models.arXiv preprint arXiv:2210.08402,

    arXiv

  9. [23]

    Zero123++: a single image to consistent multi-view diffusion base model.arXiv preprint arXiv:2310.15110,

    Ruoxi Shi, Hansheng Chen, Zhuoyang Zhang, Minghua Liu, Chao Xu, Xinyue Wei, Linghao Chen, Chong Zeng, and Hao Su. Zero123++: a single image to consistent multi-view diffusion base model.arXiv preprint arXiv:2310.15110,

    arXiv

  10. [24]

    Motionstone: Decoupled motion intensity modulation with diffusion transformer for image-to-video generation.arXiv preprint arXiv:2412.05848,

    Shuwei Shi, Biao Gong, Xi Chen, Dandan Zheng, Shuai Tan, Zizheng Y ang, Y uyuan Li, Jingwen He, Kecheng Zheng, Jingdong Chen, et al. Motionstone: Decoupled motion intensity modulation with diffusion transformer for image-to-video generation.arXiv preprint arXiv:2412.05848,

    arXiv

  11. [25]

    Autoregressive model beats diffusion: Llama for scalable image generation.arXiv preprint arXiv:2406.06525,

    Peize Sun, Yi Jiang, Shoufa Chen, Shilong Zhang, Bingyue Peng, Ping Luo, and Zehuan Y uan. Autoregressive model beats diffusion: Llama for scalable image generation.arXiv preprint arXiv:2406.06525,

    arXiv

  12. [26]

    Mimir: Improving video diffusion models for precise text understanding.arXiv preprint arXiv:2412.03085, 2024a

    Shuai Tan, Biao Gong, Y utong Feng, Kecheng Zheng, Dandan Zheng, Shuwei Shi, Y ujun Shen, Jingdong Chen, and Ming Y ang. Mimir: Improving video diffusion models for precise text understanding.arXiv preprint arXiv:2412.03085, 2024a. Shuai Tan, Biao Gong, Xiang Wang, Shiwei Zhan...

    arXiv

  13. [27]

    Metamorph: Multimodal understanding and generation via instruction tuning.arXiv preprint arXiv:2412.14164,

    Shengbang Tong, David Fan, Jiachen Zhu, Y unyang Xiong, Xinlei Chen, Koustuv Sinha, Michael Rabbat, Y ann LeCun, Saining Xie, and Zhuang Liu. Metamorph: Multimodal understanding and generation via instruction tuning.arXiv preprint arXiv:2412.14164,

    arXiv

  14. [28]

    Llama: Open and efficient foundation language models.arXiv preprint arXiv:2302.13971,

    Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models.arXiv preprint arXiv:2302.13971,

    arXiv

  15. [29]

    Qwen2-vl: Enhancing vision-language model’s perception of the world at any resolution.arXiv preprint arXiv:2409.12191, 2024a

    Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, et al. Qwen2-vl: Enhancing vision-language model’s perception of the world at any resolution.arXiv preprint arXiv:2409.12191, 2024a. Wen Wang, Qiuyu Wang, Kech...

    arXiv

  16. [2012]

    doi: 10.1109/CVPR.2012.6247954. OpenAI. Gpt-4o system card, 2024.https://arxiv.org/abs/2410.21276. OpenAI. Introducing 4o image generation.https://openai.com/index/introducing-4o-image-generation/,

    2012

  17. [2016]

    Generating multi-image synthetic data for text-to-image customization.arXiv preprint arXiv:2502.01720,

    Nupur Kumari, Xi Yin, Jun-Y an Zhu, Ishan Misra, and Samaneh Azadi. Generating multi-image synthetic data for text-to-image customization.arXiv preprint arXiv:2502.01720,

  18. [2022]

    Pixart-alpha: Fast training of diffusion transformer for photorealistic text-to-image synthesis.arXiv preprint arXiv:2310.00426,

    Junsong Chen, Jincheng Y u, Chongjian Ge, Lewei Y ao, Enze Xie, Y ue Wu, Zhongdao Wang, James Kwok, Ping Luo, Huchuan Lu, et al. Pixart-alpha: Fast training of diffusion transformer for photorealistic text-to-image synthesis.arXiv preprint arXiv:2310.00426,

    arXiv

  19. [2023]

    Qwen2.5-vl technical report, 2025.https://arxiv.org/abs/2502.13923

    Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, Humen Zhong, Y uanzhi Zhu, Mingkun Y ang, Zhaohai Li, Jianqiang Wan, Pengfei Wang, Wei Ding, Zheren Fu, Yiheng Xu, Jiabo Y e, Xi Zhang, Tianbao Xie, Zesen Cheng, ...

    2025 arXiv

  20. [2024]

    Dreamllm: Synergistic multimodal comprehension and creation.arXiv preprint arXiv:2309.11499,

    Runpei Dong, Chunrui Han, Y uang Peng, Zekun Qi, Zheng Ge, Jinrong Y ang, Liang Zhao, Jianjian Sun, Hongyu Zhou, Haoran Wei, et al. Dreamllm: Synergistic multimodal comprehension and creation.arXiv preprint arXiv:2309.11499,

    arXiv

  21. [2025]

    Expanding performance boundaries of open-source multimodal models with model, data, and test-time scaling.arXiv preprint arXiv:2412.05271, 2024b

    Zhe Chen, Weiyun Wang, Y ue Cao, Y angzhou Liu, Zhangwei Gao, Erfei Cui, Jinguo Zhu, Shenglong Y e, Hao Tian, Zhaoyang Liu, et al. Expanding performance boundaries of open-source multimodal models with model, data, and test-time scaling.arXiv preprint arXiv:2412.05271, 2024b. ...

    arXiv

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Reviewed August 16, 2026 · model on record in the stance chip above.