Qwen3.8-Omni-Flash is a natively multimodal agentic model designed for real‑world multimodal productivity, offering enhanced multimodal understanding, reasoning, and long‑horizon agentic task performance. It builds on a sparse mixture‑of‑experts architecture, extends its context window to one million tokens, and supports long‑context multimodal reasoning and planning. The release includes Qwen-MM-Plugins for native audio and video support and Qwen-Live-Harness for building responsive, real‑time multimodal agents, with extensive evaluations confirming strong performance across multimodal tasks.
By Qwen Team
The paper surveys how large multimodal models (LMMs) enhance agentic frameworks that combine perception, memory, reasoning, planning, and action. It examines the integration of multiple modalities—text, images, audio, and video—through delegated, late‑fusion, and early‑fusion architectures, and maps these designs to agent capabilities. The survey also reviews multimodal agentic systems in robotics, web navigation, multimedia content creation, and video understanding, evaluating performance, efficiency, and scalability trade‑offs.
By Neel Mokaria, Rishie Raj, Dheeraj Baiju, Xiaoqian Shen, Shraman Pramanick, Kevin Qinghong Lin, Arda Senocak, Mike Zheng Shou, Philip Torr, Mohamed Elhoseiny, Yapeng Tian, Ruohan Gao, Salman Khan, Sayan Nag, Sanjoy Chowdhury, Dinesh Manocha
arXiv:2602. 22897v3 Announce Type: replace Abstract: Human intelligence naturally intertwines omni-modal perception -- spanning vision, audio, and language -- with complex reasoning and tool usage to interact with the world.
By Xiaoxi Li, Wenxiang Jiao, Jiarui Jin, Haoxuan Li, Hao Wang, Shijian Wang, Guanting Dong, Jiajie Jin, Yinuo Wang, Yuan Lu, Ji-Rong Wen, Zhicheng Dou, Zhouchen Lin
arXiv:2607. 18171v1 Announce Type: new Abstract: Real-time multimodal applications, including voice agents and interactive video generation, compose heterogeneous models into pipelines whose efficient deployment requires application-specific decisions about placement, streaming, and intra-model parallelism.
By Krish Agarwal, Zhuoming Chen, Yanyuan Qin, Zhenyu Gu, Atri Rudra, Beidi Chen
arXiv:2609.15863v1 Announce Type: new
Abstract: Video diffusion models are stochastic and hard to control: precise content often requires repeated sampling without guaranteed success, and long-horizo...
By Xiaofeng Mao, Peijia Lin, Shaohao Rui, Yibo Zhang, Haibin Wan, Weijie Ma
arXiv:2606. 12688v1 Announce Type: cross Abstract: We are entering a new era of composite model architectures that integrate diverse components such as vision encoders, language backbones, diffusion and flow heads, audio codecs, action generators, and world-model predictors.
By Atindra Jha, Naomi Sagan, Keisuke Kamahori, Irmak Sivgin, Rohan Sanda, Steven Gao, Mark Horowitz, Luke Zettlemoyer, Olivia Hsu, Jure Leskovec, Baris Kasikci, Stephanie Wang
The technical report introduces Gander, an end‑to‑end model that integrates omni perception, real‑time interaction, and agentic capabilities into a single framework. Unlike traditional turn‑based systems, Gander continuously processes streaming inputs from video, speech, and text, enabling natural full‑duplex interaction in both everyday conversations and workflow‑oriented scenarios. Its architecture features a Cerebellum‑Brain collaboration—where the Cerebellum handles real‑time interaction and omni conversational tasks while the Brain manages complex reasoning—and a streaming Thinker‑Talker design that flattens inputs and outputs into an ordered token stream for low‑latency, continuous dialogue. Evaluations across conversational ability, omni understanding, interactive capability, and agentic intelligence show that Gander matches state‑of‑the‑art open‑source models in spoken dialogue while maintaining robust performance in noisy, multi‑party, and backchannel environments.
By Orantqing, Shengpeng Ji, Junlong Tong, Jialong Zuo, Dongjie Fu, Di Cao, Yangzhuo Li, Shangda Wu, Franz, Evan, Theron Veyra, Changhao Pan, Jingyu Lu, Dongchao Yang, Zhifei Xie, Yang Tan, Xiaoyu Shen, Xiaoda Yang, Wenfu Wang, Teddy Sun, Steve Yves, Zhou Zhao
arXiv:2606. 13707v1 Announce Type: new Abstract: The recent success of agent swarms has shifted the paradigm of large language model (LLM)-based agents from single-agent workflows to multi-agent systems, highlighting the importance of agent orchestration for task decomposition and collaboration.
By Fan Zhang, Vireo Zhang, Shengju Qian, Haoxuan Li, Hao Wu, Jinyang Wu, Donghao Zhou, Zhihong Zhu, Zheng Lian, Xin Wang, Pheng-Ann Heng
arXiv:2607. 08497v1 Announce Type: cross Abstract: Recent unified multimodal models show a single architecture can jointly perform vision/language understanding and image generation/editing.
By Feng Wang, Canmiao Fu, Zhipeng Huang, Chen Li, Jing Lyu, Ge Li
OmniAssistBench is a new benchmark for evaluating omni-modal large language models (Omni-LLMs) as real‑time video assistants that actively guide users toward goals. The benchmark addresses the challenge of dynamic interaction paths by providing models with predefined priors from source videos, forcing them to follow the same routes as users. The dataset was constructed by reverse‑engineering existing Internet videos into multi‑turn clips, a process that required over 1,000 expert person‑hours. Results show that proprietary Gemini‑3‑Pro scores 66.4/100 while open‑source Qwen3‑Omni‑Instruct scores 51.2, revealing that current models often give incorrect or incomplete answers, struggle with visual prompts, and fail to maintain context or delay responses until target events.
By Xianyun Sun, Chaoyou Fu, Zhengye Zhang, Feiyang Duan, Qingyuan Cao, Yonghui Niu, Sihang Yuan, Ge Zhang, Caifeng Shan
WeAgent-MMGenEdit is a comprehensive framework for multimodal agentic image generation and editing that addresses the unreliability of current models when prompts require external world knowledge. It introduces a multimodal harness with persistent evidence management, a scalable data construction pipeline producing 23K supervised trajectories and 14.7K RL tasks, and a bilingual benchmark (WeBench-MMGenEdit) for knowledge-intensive generation and multi-image editing. Post‑training methods based on SFT and RL further refine the agent policy and image backend, enabling a 30B‑parameter policy to outperform similarly sized models and approach the performance of a 1T‑parameter agent.
By Hui Zhang, Zongkai Liu, Liqiang Niu, Juntao Liu, Han Li, Zhen Cao, Wenchao Chen, Chengduo Zhao, Fandong Meng
arXiv:2609.18323v1 Announce Type: new
Abstract: Recent Omni-Modal Generative Models (Omni-Models) have advanced content generation toward unified modeling of text, images, video, and audio. MiniMax-H...
By Haoyu Zhao, Zihao Zhao, Tianyu Deng, Ziqin Xu, Zihao Zhang, Xudong Wang, Jinxiang Guo, Chen Gao, Ziyi Ye, Yeying Jin, Jiaxi Gu, Zuxuan Wu, Shuicheng Yan