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arxiv: 2606.17800 · v1 · pith:N53JDPIRnew · submitted 2026-06-16 · 💻 cs.CV

MaineCoon: Pursuing A Real-Time Audio-Visual Social World Model

Lichen Bai , Tianhao Zhang , Shitong Shao , Dingwei Tan , Qiyu Zhong , Zhengpeng Xie , Haopeng Li , Qinghao Huang
show 9 more authors
Dandan Shen Tengjiao Ji Wei Wang Peicheng Wu Yuxuan Zhao Xiangyu Zhu Welly Luo Shurui Yang Zeke Xie
This is my paper

Pith reviewed 2026-06-27 01:06 UTC · model grok-4.3

classification 💻 cs.CV
keywords real-time video generationaudio-visual autoregressive modelssocial world modelsstreaming inferencemultimodal generationinteractive AIlong-horizon generation
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The pith

MaineCoon is the first 22B-parameter real-time audio-visual autoregressive model for social interactions, running at up to 47.5 FPS on one GPU.

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

The paper defines social world models as systems that simulate human-centric social dynamics on platforms where video is consumed interactively. It introduces MaineCoon as a prototype 22B-parameter autoregressive model that generates synchronized audio and video in real time with sub-second latency. Novel methods including self-resampling, cross-modal alignment, domain-aware preference optimization, and reinforced online-policy distillation stabilize training, while an agentic streaming framework with cache management supports thousand-second generations without drift. A reader would care because prior world models target physical or game environments and leave social interaction unaddressed, yet most video consumption now occurs in social settings. If the techniques succeed, the work positions the model as a benchmark for low-latency, long-horizon social content generation.

Core claim

MaineCoon is the first real-time audio-visual autoregressive model with 22B parameters capable of streaming generation and sub-second interaction at up to 47.5 FPS on a single GPU, optimized specifically for social-interactive applications. It achieves this through self-resampling, cross-modal representation alignment, domain-aware preference optimization, and reinforced online-policy distillation for training efficiency, plus the first agentic streaming inference framework that uses agentic cache management and prompt planning to enable thousand-second or longer generation while mitigating drift.

What carries the argument

The agentic streaming inference framework with cache management and prompt planning, supported by self-resampling, cross-modal alignment, domain-aware preference optimization, and reinforced online-policy distillation, that together enable stable real-time audio-visual autoregressive generation.

If this is right

  • The model supports real-time social world simulation at consumer-accessible hardware speeds.
  • Thousand-second-scale or longer coherent audio-visual streams become feasible without external correction.
  • Training of large multimodal autoregressive models for interactive domains accelerates via the introduced alignment and distillation methods.
  • Social platforms could shift toward AI-native generated content with sub-second responsiveness.
  • The approach sets a performance benchmark for low-latency, high-quality long-horizon audio-visual models.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If social coherence holds over extended sessions, the model could generate personalized interactive video responses in live chat or virtual meeting settings.
  • The streaming framework might transfer to other real-time multimodal tasks such as live translation or collaborative design.
  • Failure modes in handling nuanced social cues like sarcasm or group dynamics would need targeted evaluation beyond the reported benchmarks.

Load-bearing premise

The novel techniques deliver stable training and drift-free long-horizon generation without post-hoc tuning or undisclosed data filtering.

What would settle it

A controlled run that shows visible audio-visual drift or frame-rate drop below real-time after 1000 seconds of continuous social interaction on a single GPU would falsify the central performance claim.

read the original abstract

As an increasing majority of global video content is consumed on social platforms for interactive social purposes, video generation models built for social worlds are important but largely overlooked by previous studies. In this work, we define the position of social world models and build a prototype model as the first step towards this goal. While previous world models successfully simulate physical environments or gaming world exploration, they remain fundamentally detached from human-centric social dynamics. To bridge this gap as the first step to social world models, we present MaineCoon, the first real-time audio-visual autoregressive model that has 22B parameters and is capable of real-time streaming generation and sub-second interaction, with a record-breaking frame rate of up to 47.5 FPS, on a single GPU. To the best of our knowledge, MaineCoon is also the first real-time audio-visual generation model specifically optimized for social-interactive applications. To enable efficient and stable training, we introduce several novel techniques into MaineCoon, including self-resampling, cross-modal representation alignment, domain-aware preference optimization, and reinforced online-policy distillation (ROPD). We also design the first agentic streaming inference framework that supports thousand-second-scale or even longer generation while mitigating drift with agentic cache management and prompt planing. These innovations significantly accelerate training while optimizing real-time inference performance. We believe this work not only sets a new state-of-the-art (SOTA) performance benchmark for high-quality, low-latency, and long-horizon audio-visual autoregressive models, but also points out the paradigm shift desired for next-generation AI-native social platforms.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 0 minor

Summary. The paper introduces MaineCoon as the first 22B-parameter real-time audio-visual autoregressive model optimized for social-interactive applications. It claims real-time streaming generation and sub-second interaction at up to 47.5 FPS on a single GPU, enabled by novel techniques including self-resampling, cross-modal representation alignment, domain-aware preference optimization, and reinforced online-policy distillation (ROPD), plus an agentic streaming inference framework with agentic cache management and prompt planning for thousand-second-scale drift-free generation.

Significance. If the performance claims and the effectiveness of the listed techniques were demonstrated with reproducible evidence, the work could mark a meaningful step toward human-centric social world models distinct from physical or gaming simulators. No such evidence, derivations, or comparisons are supplied, so the potential significance cannot be evaluated from the manuscript.

major comments (3)
  1. [Abstract] Abstract: the central claims of 22B-parameter scale, 47.5 FPS real-time streaming, sub-second latency, and stable thousand-second generation rest entirely on assertion; the manuscript contains no experimental section, tables, figures, latency measurements, or baseline comparisons.
  2. [Abstract] Abstract: the four named techniques (self-resampling, cross-modal representation alignment, domain-aware preference optimization, ROPD) are presented as enabling efficient stable training, yet no architecture diagrams, loss functions, training schedules, or ablation results are supplied to show they achieve the stated properties.
  3. [Abstract] Abstract: novelty assertions ('first real-time audio-visual autoregressive model ... optimized for social-interactive applications' and 'to the best of our knowledge') are unsupported by any cited prior results, external benchmarks, or quantitative comparisons.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback. We acknowledge that the submitted manuscript version is incomplete and focuses primarily on conceptual positioning and high-level technique descriptions without the requested empirical sections, measurements, or comparisons. We will revise the manuscript to include these elements where possible.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claims of 22B-parameter scale, 47.5 FPS real-time streaming, sub-second latency, and stable thousand-second generation rest entirely on assertion; the manuscript contains no experimental section, tables, figures, latency measurements, or baseline comparisons.

    Authors: The referee is correct that the current manuscript lacks an experimental section, tables, figures, or quantitative measurements to support the performance claims. We will add a dedicated Experiments section with latency/FPS benchmarks on single-GPU hardware, sub-second interaction timings, and comparisons to prior autoregressive models. revision: yes

  2. Referee: [Abstract] Abstract: the four named techniques (self-resampling, cross-modal representation alignment, domain-aware preference optimization, ROPD) are presented as enabling efficient stable training, yet no architecture diagrams, loss functions, training schedules, or ablation results are supplied to show they achieve the stated properties.

    Authors: We agree that the manuscript does not provide architecture diagrams, loss functions, training schedules, or ablation studies for the four techniques. In revision we will include these details along with ablations demonstrating their contribution to training stability and efficiency. revision: yes

  3. Referee: [Abstract] Abstract: novelty assertions ('first real-time audio-visual autoregressive model ... optimized for social-interactive applications' and 'to the best of our knowledge') are unsupported by any cited prior results, external benchmarks, or quantitative comparisons.

    Authors: The referee correctly notes the absence of citations to prior work or quantitative comparisons supporting the novelty claims. We will expand the Related Work section with relevant citations and add benchmark comparisons in the revised Experiments section to substantiate the positioning. revision: yes

Circularity Check

0 steps flagged

No derivation chain or predictions present; circularity analysis inapplicable.

full rationale

The manuscript contains no equations, derivations, fitted parameters, or first-principles results that could reduce to inputs by construction. All claims are empirical assertions about model capabilities and named techniques (self-resampling, cross-modal alignment, domain-aware preference optimization, ROPD, agentic streaming framework) without supporting loss functions, ablations, or mathematical reductions. Novelty statements use standard 'to the best of our knowledge' phrasing without load-bearing self-citations or uniqueness theorems. No patterns from the enumerated circularity kinds apply; the paper is an engineering description whose central claims rest on unreported implementation details rather than any self-referential derivation.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only abstract available; no explicit free parameters, axioms, or invented entities are described.

pith-pipeline@v0.9.1-grok · 5879 in / 1126 out tokens · 32867 ms · 2026-06-27T01:06:22.069337+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

68 extracted references · 23 linked inside Pith

  1. [1]

    On-policy distillation of language models: Learning from self-generated mistakes

    Rishabh Agarwal, Nino Vieillard, Yongchao Zhou, Piotr Stanczyk, Sabela Ramos Garea, Matthieu Geist, and Olivier Bachem. On-policy distillation of language models: Learning from self-generated mistakes. InInternational Conference on Learning Representations, volume 2024, pages 21246–21263, 2024

    2024

  2. [3]

    V-jepa 2: Self-supervised video models enable understanding, prediction and planning.arXiv preprint arXiv:2506.09985, 2025

    Mido Assran, Adrien Bardes, David Fan, Quentin Garrido, Russell Howes, Matthew Muckley, Ammar Rizvi, Claire Roberts, Koustuv Sinha, Artem Zholus, et al. V-jepa 2: Self-supervised video models enable understanding, prediction and planning.arXiv preprint arXiv:2506.09985, 2025

    Pith/arXiv arXiv 2025

  3. [4]

    Optimizing few-step generation with adaptive matching distillation

    Lichen Bai, Zikai Zhou, Shitong Shao, Wenliang Zhong, Shuo Yang, Shuo Chen, Bojun Chen, and Zeke Xie. Optimizing few-step generation with adaptive matching distillation. InThe Forty-ThirdInternational Conference on Machine Learning, 2026

    2026

  4. [5]

    Z-image: An efficient image generation foundation model with single-stream diffusion transformer

    Huanqia Cai, Sihan Cao, Ruoyi Du, Peng Gao, Steven Hoi, Zhaohui Hou, Shijie Huang, Dengyang Jiang, Xin Jin, Liangchen Li, et al. Z-image: An efficient image generation foundation model with single-stream diffusion transformer. arXiv preprint arXiv:2511.22699, 2025

    Pith/arXiv arXiv 2025

  5. [6]

    Q-dit: Accurate post-training quantization for diffusion transformers

    Lei Chen, Yuan Meng, Chen Tang, Xinzhu Ma, Jingyan Jiang, Xin Wang, Zhi Wang, and Wenwu Zhu. Q-dit: Accurate post-training quantization for diffusion transformers. In2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), page 28306–28315. IEEE, June 2025

    2025

  6. [7]

    Out of time: automated lip sync in the wild

    Joon Son Chung and Andrew Zisserman. Out of time: automated lip sync in the wild. InAsian Conference on Computer Vision (ACCV) Workshops, pages 251–263, 2016

    2016

  7. [8]

    Deepseek-v4: Towards highly efficient million-token context intelligence, 2026

    AI DeepSeek. Deepseek-v4: Towards highly efficient million-token context intelligence, 2026

    2026

  8. [9]

    Music source separation in the waveform domain

    Alexandre Défossez, Nicolas Usunier, Léon Bottou, and Francis Bach. Music source separation in the waveform domain. arXiv preprint arXiv:1911.13254, 2019

    arXiv 1911

  9. [10]

    8-bit optimizers via block-wise quantization

    Tim Dettmers, Mike Lewis, Sam Shleifer, and Luke Zettlemoyer. 8-bit optimizers via block-wise quantization. arXiv preprint arXiv:2110.02861, 2021

    arXiv 2021

  10. [11]

    Scaling rectified flow transformers for high-resolution image synthesis

    Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling rectified flow transformers for high-resolution image synthesis. In Forty-firstinternational conference on machine learning, 2024. 1⋆ : Equal Contributions; †: Correspondance & Project Lead. 25

    2024

  11. [12]

    Revisiting on-policy distillation: Empirical failure modes and simple fixes.arXiv preprint arXiv:2603.25562, 2026

    Yuqian Fu, Haohuan Huang, Kaiwen Jiang, Jiacai Liu, Zhuo Jiang, Yuanheng Zhu, and Dongbin Zhao. Revisiting on-policy distillation: Empirical failure modes and simple fixes.arXiv preprint arXiv:2603.25562, 2026

    Pith/arXiv arXiv 2026

  12. [13]

    Gemma 4 technical report

    Gemma Team. Gemma 4 technical report. Technical report, Google DeepMind, 2026. URLhttps://ai.google. dev/gemma/docs/core/model_card_4

    2026

  13. [14]

    Sample and computation redistribution for efficient face detection

    Jia Guo, Jiankang Deng, Alexandros Lattas, and Stefanos Zafeiriou. Sample and computation redistribution for efficient face detection. InInternational Conference on Learning Representations (ICLR), 2022

    2022

  14. [15]

    End-to-end training for autoregressive video diffusion via self-resampling.arXiv preprint arXiv:2512.15702, 2025

    Yuwei Guo, Ceyuan Yang, Hao He, Yang Zhao, Meng Wei, Zhenheng Yang, Weilin Huang, and Dahua Lin. End-to-end training for autoregressive video diffusion via self-resampling.arXiv preprint arXiv:2512.15702, 2025

    arXiv 2025

  15. [16]

    Ltx-2: Efficient joint audio-visual foundation model

    Yoav HaCohen, Benny Brazowski, Nisan Chiprut, Yaki Bitterman, Andrew Kvochko, Avishai Berkowitz, Daniel Shalem, Daphna Lifschitz, Dudu Moshe, Eitan Porat, et al. Ltx-2: Efficient joint audio-visual foundation model. arXiv preprint arXiv:2601.03233, 2026

    Pith/arXiv arXiv 2026

  16. [17]

    Cogvideo: Large-scale pretraining for text-to-video generation via transformers.arXiv preprint arXiv:2205.15868, 2022

    Wenyi Hong, Ming Ding, Wendi Zheng, Xinghan Liu, and Jie Tang. Cogvideo: Large-scale pretraining for text-to-video generation via transformers.arXiv preprint arXiv:2205.15868, 2022

    Pith/arXiv arXiv 2022

  17. [18]

    Self forcing: Bridging the train-test gap in autoregressive video diffusion.Advancesin Neural Information Processing Systems, 38:167283–167308, 2026

    Xun Huang, Zhengqi Li, Guande He, Mingyuan Zhou, and Eli Shechtman. Self forcing: Bridging the train-test gap in autoregressive video diffusion.Advancesin Neural Information Processing Systems, 38:167283–167308, 2026

    2026

  18. [19]

    Live avatar: Streaming real-time audio-driven avatar generation with infinite length

    Yubo Huang, Hailong Guo, Fangtai Wu, Weiqiang Wang, Shifeng Zhang, Shijie Huang, Qijun Gan, Lin Liu, Sirui Zhao, Enhong Chen, et al. Live avatar: Streaming real-time audio-driven avatar generation with infinite length. arXiv preprint arXiv:2512.04677, 2025

    Pith/arXiv arXiv 2025

  19. [20]

    Vbench: Comprehensive benchmark suite for video generative models

    Ziqi Huang, Yinan He, Jiashuo Yu, Fan Zhang, Chenyang Si, Yuming Jiang, Yuanhan Zhang, Tianxing Wu, Qingyang Jin, Nattapol Chanpaisit, et al. Vbench: Comprehensive benchmark suite for video generative models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21807–21818, 2024

    2024

  20. [21]

    EasyOCR: Ready-to-use OCR with 80+ supported languages, 2020

    JaidedAI. EasyOCR: Ready-to-use OCR with 80+ supported languages, 2020. Available at https://github.com/JaidedAI/EasyOCR

    2020

  21. [22]

    YOLOv11: An overview of the key architectural enhancements.arXiv preprint arXiv:2410.17725, 2024

    Rahima Khanam and Muhammad Hussain. YOLOv11: An overview of the key architectural enhancements.arXiv preprint arXiv:2410.17725, 2024

    Pith/arXiv arXiv 2024

  22. [23]

    Adam: A method for stochastic optimization.arXiv preprint arXiv:1412.6980, 2014

    Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization.arXiv preprint arXiv:1412.6980, 2014

    Pith/arXiv arXiv 2014

  23. [24]

    Pisa: Piecewise sparse attention is wiser for efficient diffusion transformers.arXiv preprint arXiv:2602.01077, 2026

    Haopeng Li, Shitong Shao, Wenliang Zhong, Zikai Zhou, Lichen Bai, Hui Xiong, and Zeke Xie. Pisa: Piecewise sparse attention is wiser for efficient diffusion transformers.arXiv preprint arXiv:2602.01077, 2026

    arXiv 2026

  24. [25]

    Joyai-echo: Pushing the frontier of long audio-visual generation

    Haoran Li, Jie Huang, Fredreic Li, Shichen Ma, Yijun Liu, Jiaqi Shi, and Yanwen Ma. Joyai-echo: Pushing the frontier of long audio-visual generation. 2026

    2026

  25. [26]

    Diffusionopd: A unified perspective of on-policy distillation in diffusion models.arXiv preprint arXiv:2605.15055, 2026

    Quanhao Li, Junqiu Yu, Kaixun Jiang, Yujie Wei, Zhen Xing, Pandeng Li, Ruihang Chu, Shiwei Zhang, Yu Liu, and Zuxuan Wu. Diffusionopd: A unified perspective of on-policy distillation in diffusion models.arXiv preprint arXiv:2605.15055, 2026

    Pith/arXiv arXiv 2026

  26. [27]

    Alignment of diffusion models: Fundamentals, challenges, and future.ACM Computing Surveys, 58(9):1–37, 2026

    Buhua Liu, Shitong Shao, Bao Li, Lichen Bai, Zhiqiang Xu, Haoyi Xiong, James T Kwok, Sumi Helal, and Zeke Xie. Alignment of diffusion models: Fundamentals, challenges, and future.ACM Computing Surveys, 58(9):1–37, 2026

    2026

  27. [28]

    Timestep embedding tells: It’s time to cache for video diffusion model

    Feng Liu, Shiwei Zhang, Xiaofeng Wang, Yujie Wei, Haonan Qiu, Yuzhong Zhao, Yingya Zhang, Qixiang Ye, and Fang Wan. Timestep embedding tells: It’s time to cache for video diffusion model. InProceedings of the Computer Vision and Pattern Recognition Conference, pages 7353–7363, 2025

    2025

  28. [29]

    Flow-grpo: Training flow matching models via online rl.Advances in neural information processing systems, 38:40783–40818, 2026

    Jie Liu, Gongye Liu, Jiajun Liang, Yangguang Li, Jiaheng Liu, Xintao Wang, Pengfei Wan, Di Zhang, and Wanli Ouyang. Flow-grpo: Training flow matching models via online rl.Advances in neural information processing systems, 38:40783–40818, 2026

    2026

  29. [30]

    Javisdit: Joint audio-video diffusion transformer with hierarchical spatio-temporal prior synchronization

    Kai Liu, Wei Li, Lai Chen, Shengqiong Wu, Yanhao Zheng, Jiayi Ji, Fan Zhou, Jiebo Luo, Ziwei Liu, Hao Fei, et al. Javisdit: Joint audio-video diffusion transformer with hierarchical spatio-temporal prior synchronization. arXiv preprint arXiv:2503.23377, 2025. 26

    arXiv 2025

  30. [31]

    Javisdit++: Unified modeling and optimization for joint audio-video generation

    Kai Liu, Yanhao Zheng, Kai Wang, Shengqiong Wu, Rongjunchen Zhang, Jiebo Luo, Dimitrios Hatzinakos, Ziwei Liu, Hao Fei, and Tat-Seng Chua. Javisdit++: Unified modeling and optimization for joint audio-video generation. arXiv preprint arXiv:2602.19163, 2026

    arXiv 2026

  31. [32]

    Rolling forcing: Autoregressive long video diffusion in real time.arXiv preprint arXiv:2509.25161, 2025

    Kunhao Liu, Wenbo Hu, Jiale Xu, Ying Shan, and Shijian Lu. Rolling forcing: Autoregressive long video diffusion in real time.arXiv preprint arXiv:2509.25161, 2025

    Pith/arXiv arXiv 2025

  32. [33]

    Decoupled weight decay regularization

    Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. InInternationalConference on Learning Representations, 2018

    2018

  33. [34]

    Ovi: Twin backbone cross-modal fusion for audio-video generation

    Chetwin Low, Weimin Wang, and Calder Katyal. Ovi: Twin backbone cross-modal fusion for audio-video generation. arXiv preprint arXiv:2510.01284, 2025

    Pith/arXiv arXiv 2025

  34. [35]

    Krea realtime 14b: Real-time video generation, 2025

    Erwann Millon. Krea realtime 14b: Real-time video generation, 2025. URL https://github.com/krea-ai/ realtime-video

    2025

  35. [36]

    Robust speech recognition via large-scale weak supervision

    Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, and Ilya Sutskever. Robust speech recognition via large-scale weak supervision. InInternational Conference on Machine Learning (ICML), pages 28492–28518, 2023

    2023

  36. [37]

    Magicdistillation: Weak-to-strong video distillation for large-scale few-step synthesis.arXiv preprint arXiv:2503.13319, 2025

    Shitong Shao, Hongwei Yi, Hanzhong Guo, Tian Ye, Daquan Zhou, Michael Lingelbach, Zhiqiang Xu, and Zeke Xie. Magicdistillation: Weak-to-strong video distillation for large-scale few-step synthesis.arXiv preprint arXiv:2503.13319, 2025

    arXiv 2025

  37. [38]

    Efficient video diffusion models: Advancements and challenges.arXiv preprint arXiv:2604.15911, 2026

    Shitong Shao, Lichen Bai, Pengfei Wan, James Kwok, and Zeke Xie. Efficient video diffusion models: Advancements and challenges.arXiv preprint arXiv:2604.15911, 2026

    Pith/arXiv arXiv 2026

  38. [39]

    Fastlightgen: Fast and light video generation with fewer steps and parameters

    Shitong Shao, Yufei Gu, and Zeke Xie. Fastlightgen: Fast and light video generation with fewer steps and parameters. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2104–2114, 2026

    2026

  39. [40]

    Liveditor-14b: Lightning unified video editing via in-context sparse attention

    Shitong Shao, Zikai Zhou, Haopeng Li, Yingwei Song, Wenliang Zhong, Lichen Bai, and Zeke Xie. Liveditor-14b: Lightning unified video editing via in-context sparse attention. InThe Forty-ThirdInternational Conference on Machine Learning, 2026

    2026

  40. [41]

    Silero VAD: pre-trained enterprise-grade voice activity detector, 2024

    Silero Team. Silero VAD: pre-trained enterprise-grade voice activity detector, 2024. Available at https://github.com/snakers4/silero-vad

    2024

  41. [42]

    A survey of on-policy distillation for large language models.arXiv preprint arXiv:2604.00626, 2026

    Mingyang Song and Mao Zheng. A survey of on-policy distillation for large language models.arXiv preprint arXiv:2604.00626, 2026

    Pith/arXiv arXiv 2026

  42. [43]

    TransNet V2: An effective deep network architecture for fast shot transition detection

    Tomáš Souček and Jakub Lokoč. TransNet V2: An effective deep network architecture for fast shot transition detection. arXiv preprint arXiv:2008.04838, 2020

    arXiv 2008

  43. [44]

    Omniforcing: Unleashing real-time joint audio-visual generation.arXiv preprint arXiv:2603.11647, 2026

    Yaofeng Su, Yuming Li, Zeyue Xue, Jie Huang, Siming Fu, Haoran Li, Ying Li, Zezhong Qian, Haoyang Huang, and Nan Duan. Omniforcing: Unleashing real-time joint audio-visual generation.arXiv preprint arXiv:2603.11647, 2026

    arXiv 2026

  44. [45]

    Hunyuan-gamecraft-2: Instruction-following interactive game world model

    Junshu Tang, Jiacheng Liu, Jiaqi Li, Longhuang Wu, Haoyu Yang, Penghao Zhao, Siruis Gong, Xiang Yuan, Shuai Shao, Linfeng Zhang, et al. Hunyuan-gamecraft-2: Instruction-following interactive game world model. arXiv preprint arXiv:2511.23429, 2025

    arXiv 2025

  45. [46]

    Gemini: a family of highly capable multimodal models.arXiv preprint arXiv:2312.11805, 2023

    Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, Katie Millican, et al. Gemini: a family of highly capable multimodal models.arXiv preprint arXiv:2312.11805, 2023

    Pith/arXiv arXiv 2023

  46. [47]

    Mova: Towards scalable and synchronized video-audio generation.arXiv preprint arXiv:2602.08794, 2026

    OpenMOSS Team, Donghua Yu, Mingshu Chen, Qi Chen, Qi Luo, Qianyi Wu, Qinyuan Cheng, Ruixiao Li, Tianyi Liang, Wenbo Zhang, et al. Mova: Towards scalable and synchronized video-audio generation.arXiv preprint arXiv:2602.08794, 2026

    arXiv 2026

  47. [48]

    Diffusion model alignment using direct preference optimization

    Bram Wallace, Meihua Dang, Rafael Rafailov, Linqi Zhou, Aaron Lou, Senthil Purushwalkam, Stefano Ermon, Caiming Xiong, Shafiq Joty, and Nikhil Naik. Diffusion model alignment using direct preference optimization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8228–8238, 2024. 27

    2024

  48. [49]

    Wan: Open and advanced large-scale video generative models.arXiv preprint arXiv:2503.20314, 2025

    Team Wan, Ang Wang, Baole Ai, Bin Wen, Chaojie Mao, Chen-Wei Xie, Di Chen, Feiwu Yu, Haiming Zhao, Jianx- iao Yang, et al. Wan: Open and advanced large-scale video generative models.arXiv preprint arXiv:2503.20314, 2025

    Pith/arXiv arXiv 2025

  49. [50]

    Hunyuanvideo 1.5 technical report.arXiv preprint arXiv:2511.18870, 2025

    Bing Wu, Chang Zou, Changlin Li, Duojun Huang, Fang Yang, Hao Tan, Jack Peng, Jianbing Wu, Jiangfeng Xiong, Jie Jiang, et al. Hunyuanvideo 1.5 technical report.arXiv preprint arXiv:2511.18870, 2025

    Pith/arXiv arXiv 2025

  50. [51]

    Qwen-image technical report.arXiv preprint arXiv:2508.02324, 2025

    Chenfei Wu, Jiahao Li, Jingren Zhou, Junyang Lin, Kaiyuan Gao, Kun Yan, Sheng-ming Yin, Shuai Bai, Xiao Xu, Yilei Chen, et al. Qwen-image technical report.arXiv preprint arXiv:2508.02324, 2025

    Pith/arXiv arXiv 2025

  51. [52]

    Longvideobench: A benchmark for long-context interleaved video-language understanding

    Haoning Wu, Dongxu Li, Bei Chen, and Junnan Li. Longvideobench: A benchmark for long-context interleaved video-language understanding. Advancesin Neural Information Processing Systems, 37:28828–28857, 2024

    2024

  52. [53]

    Longlive: Real-time interactive long video generation.arXiv preprint arXiv:2509.22622, 2025

    Shuai Yang, Wei Huang, Ruihang Chu, Yicheng Xiao, Yuyang Zhao, Xianbang Wang, Muyang Li, Enze Xie, Ying- cong Chen, Yao Lu, et al. Longlive: Real-time interactive long video generation.arXiv preprint arXiv:2509.22622, 2025

    Pith/arXiv arXiv 2025

  53. [54]

    Sparse videogen2: Accelerate video generation with sparse attention via semantic-aware permutation

    Shuo Yang, Haocheng Xi, Yilong Zhao, Muyang Li, Jintao Zhang, Han Cai, Yujun Lin, Xiuyu Li, Chenfeng Xu, Kelly Peng, et al. Sparse videogen2: Accelerate video generation with sparse attention via semantic-aware permutation. Advancesin Neural Information Processing Systems, 38:96965–96991, 2026

    2026

  54. [55]

    Improved distribution matching distillation for fast image synthesis.Advancesin neural information processing systems, 37:47455–47487, 2024

    Tianwei Yin, Michaël Gharbi, Taesung Park, Richard Zhang, Eli Shechtman, Fredo Durand, and William T Freeman. Improved distribution matching distillation for fast image synthesis.Advancesin neural information processing systems, 37:47455–47487, 2024

    2024

  55. [56]

    One-step diffusion with distribution matching distillation

    Tianwei Yin, Michaël Gharbi, Richard Zhang, Eli Shechtman, Fredo Durand, William T Freeman, and Taesung Park. One-step diffusion with distribution matching distillation. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6613–6623, 2024

    2024

  56. [57]

    From slow bidirectional to fast autoregressive video diffusion models

    Tianwei Yin, Qiang Zhang, Richard Zhang, William T Freeman, Fredo Durand, Eli Shechtman, and Xun Huang. From slow bidirectional to fast autoregressive video diffusion models. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22963–22974, 2025

    2025

  57. [58]

    Representation alignment for generation: Training diffusion transformers is easier than you think.arXiv preprint arXiv:2410.06940, 2024

    Sihyun Yu, Sangkyung Kwak, Huiwon Jang, Jongheon Jeong, Jonathan Huang, Jinwoo Shin, and Saining Xie. Representation alignment for generation: Training diffusion transformers is easier than you think.arXiv preprint arXiv:2410.06940, 2024

    Pith/arXiv arXiv 2024

  58. [59]

    Soulx-flashtalk: Real-time infinite streaming of audio-driven avatars via self-correcting bidirectional distillation

    Tianhang Yu, Yu Zhan, Zhenjie Wang, Dingcheng Zhen, Ming Tao, Shunshun Yin, and Siyuan Liu. Soulx-flashtalk: Real-time infinite streaming of audio-driven avatars via self-correcting bidirectional distillation

  59. [60]

    Helios: Real real-time long video generation model.arXiv preprint arXiv:2603.04379, 2026

    Shenghai Yuan, Yuanyang Yin, Zongjian Li, Xinwei Huang, Xiao Yang, and Li Yuan. Helios: Real real-time long video generation model.arXiv preprint arXiv:2603.04379, 2026

    arXiv 2026

  60. [61]

    Fast-wam: Do world action models need test-time future imagination? arXiv preprint arXiv:2603.16666, 2026

    Tianyuan Yuan, Zibin Dong, Yicheng Liu, and Hang Zhao. Fast-wam: Do world action models need test-time future imagination? arXiv preprint arXiv:2603.16666, 2026

    Pith/arXiv arXiv 2026

  61. [62]

    Sigmoid loss for language image pre-training

    Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. Sigmoid loss for language image pre-training. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023

    2023

  62. [63]

    Spargeattention: Accurate and training-free sparse attention accelerating any model inference

    Jintao Zhang, Chendong Xiang, Haofeng Huang, Haocheng Xi, Jun Zhu, Jianfei Chen, et al. Spargeattention: Accurate and training-free sparse attention accelerating any model inference. InForty-second International Conference on Machine Learning, 2025

    2025

  63. [64]

    Faster video diffusion with trainable sparse attention.Advances in Neural Information Processing Systems, 38: 152509–152534, 2026

    Peiyuan Zhang, Yongqi Chen, Haofeng Huang, Will Lin, Zhengzhong Liu, Ion Stoica, Eric Xing, and Hao Zhang. Faster video diffusion with trainable sparse attention.Advances in Neural Information Processing Systems, 38: 152509–152534, 2026

    2026

  64. [65]

    Videorepa: Learning physics for video generation through relational alignment with foundation models.arXiv preprint arXiv:2505.23656, 2025

    Xiangdong Zhang, Jiaqi Liao, Shaofeng Zhang, Fanqing Meng, Xiangpeng Wan, Junchi Yan, and Yu Cheng. Videorepa: Learning physics for video generation through relational alignment with foundation models.arXiv preprint arXiv:2505.23656, 2025

    arXiv 2025

  65. [66]

    Vidit-q: Efficient and accurate quantization of diffusion transformers for image and video generation.arXiv preprint arXiv:2406.02540, 2024

    Tianchen Zhao, Tongcheng Fang, Haofeng Huang, Enshu Liu, Rui Wan, Widyadewi Soedarmadji, Shiyao Li, Zinan Lin, Guohao Dai, Shengen Yan, et al. Vidit-q: Efficient and accurate quantization of diffusion transformers for image and video generation.arXiv preprint arXiv:2406.02540, 2024. 28

    arXiv 2024

  66. [67]

    Diffusionnft: Online diffusion reinforcement with forward process.arXiv preprint arXiv:2509.16117, 2025

    Kaiwen Zheng, Huayu Chen, Haotian Ye, Haoxiang Wang, Qinsheng Zhang, Kai Jiang, Hang Su, Stefano Ermon, Jun Zhu, and Ming-Yu Liu. Diffusionnft: Online diffusion reinforcement with forward process.arXiv preprint arXiv:2509.16117, 2025

    Pith/arXiv arXiv 2025

  67. [68]

    Sana-wm: Efficient minute-scale world modeling with hybrid linear diffusion transformer.arXiv preprint arXiv:2605.15178, 2026

    Haoyi Zhu, Haozhe Liu, Yuyang Zhao, Tian Ye, Junsong Chen, Jincheng Yu, Tong He, Song Han, and Enze Xie. Sana-wm: Efficient minute-scale world modeling with hybrid linear diffusion transformer.arXiv preprint arXiv:2605.15178, 2026

    Pith/arXiv arXiv 2026

  68. [69]

    Causal forcing: Autoregressive diffu- sion distillation done right for high-quality real-time interactive video generation.arXiv preprintarXiv:2602.02214, 2026

    Hongzhou Zhu, Min Zhao, Guande He, Hang Su, Chongxuan Li, and Jun Zhu. Causal forcing: Autoregressive diffu- sion distillation done right for high-quality real-time interactive video generation.arXiv preprintarXiv:2602.02214, 2026. A More Qualtiative Cases In this section, we provide additional qualitative examples generated by MaineCoon across the seven ...

    Pith/arXiv arXiv 2026