arXivDaily arXiv每日学术速递 周一至周五更新

AI 大模型

多模态大模型

跨文本、图像、视频、音频等模态的大模型与学习方法。

今日/当前日期收录 15 信号源:cs.CV, cs.CL, cs.AI, cs.MM, eess.AS
2606.02800 2026-06-18 cs.CV cs.AI cs.LG cs.MM cs.RO 版本更新 专题 95

Cosmos 3: Omnimodal World Models for Physical AI

Cosmos 3:面向物理AI的全模态世界模型

NVIDIA, :, Aditi, Niket Agarwal, Arslan Ali, Jon Allen, Martin Antolini, Adeline Aubame, Alisson Azzolini, Junjie Bai, Maciej Bala, Yogesh Balaji, Josh Bapst, Aarti Basant, Mukesh Beladiya, Mohammad Qazim Bhat, Zaid Pervaiz Bhat, Dan Blick, Vanni Brighella, Han Cai, Tiffany Cai, Eric Cameracci, Jiaxin Cao, Yulong Cao, Mark Carlson, Carlos Casanova, Ting-Yun Chang, Yan Chang, Yu-Wei Chao, Prithvijit Chattopadhyay, Roshan Chaudhari, Chieh-Yun Chen, Junyu Chen, Ke Chen, Qizhi Chen, Wenkai Chen, Xiaotong Chen, Yu Chen, An-Chieh Cheng, Click Cheng, Xiu Chia, Jeana Choi, Chaeyeon Chung, Wenyan Cong, Yin Cui, Magdalena Dadela, Nalin Dadhich, Wenliang Dai, Joyjit Daw, Alperen Degirmenci, Rodrigo Vieira Del Monte, Robert Denomme, Sameer Dharur, Marco Di Lucca, Ke Ding, Wenhao Ding, Yifan Ding, Yuzhu Dong, Nicole Drumheller, Yilun Du, Aigul Dzhumamuratova, Aleksandr Efitorov, Hamid Eghbalzadeh, Naomi Eigbe, Imad El Hanafi, Hassan Eslami, Benedikt Falk, Jiaojiao Fan, Jim Fan, Amol Fasale, Sergiy Fefilatyev, Liang Feng, Francesco Ferroni, Sanja Fidler, Xiao Fu, Vikram Fugro, Prashant Gaikwad, TJ Galda, Katelyn Gao, Yihuai Gao, Wenhang Ge, Sreyan Ghosh, Arushi Goel, Vivek Goel, Akash Gokul, Rama Govindaraju, Jinwei Gu, Miguel Guerrero, Elfie Guo, Aryaman Gupta, Siddharth Gururani, Hugo Hadfield, Song Han, Ankur Handa, Zekun Hao, Mohammad Harrim, Ali Hassani, Nathan Hayes-Roth, Yufan He, Chris Helvig, Cyrus Hogg, Madison Huang, Michael Huang, Sophia Huang, Yufan Huang, Jacob Huffman, DeLesley Hutchins, Suneel Indupuru, Boris Ivanovic, Arihant Jain, Joel Jang, Ryan Ji, Yanan Jian, Dongfu Jiang, Jingyi Jin, Atharva Joshi, Nikhilesh Joshi, Pranjali Joshi, Andy Ju, Jaehun Jung, Weiwei Kang, Scott Kassekert, Jan Kautz, Ashna Khetan, Julia Kiczka, Slawek Kierat, Gwanghyun Kim, Kuno Kim, Sunny Kim, Kezhi Kong, Xin Kong, Zhifeng Kong, Tomasz Kornuta, Egor Krivov, Hui Kuang, Saurav Kumar, Chia-Wen Kuo, George Kurian, Wojciech Kutak, JF Lafleche, Himangshu Lahkar, Omar Laymoun, Jayjun Lee, Sanggil Lee, Gabriele Leone, Boyi Li, Freya Li, Jiajun Li, Jinfeng Li, Ling Li, Pengcheng Li, Shangru Li, Tingle Li, Xiaolong Li, Xuan Li, Zhaoshuo Li, Zhiqi Li, Hao Liang, Maosheng Liao, Chen-Hsuan Lin, Tsung-Yi Lin, Ming-Yu Liu, Sifei Liu, Zihan Liu, Hai Loc Lu, Xiangyu Lu, Alice Luo, Ruipu Luo, Wenjie Luo, Jiangran Lyu, Martin Ding Ma, Nic Ma, Qianli Ma, Dawid Majchrowski, Louis Marcoux, Miguel Martin, Qing Miao, Ashkan Mirzaei, Shreyas Misra, Kaichun Mo, Durra Mohsin, Hyejin Moon, Pawel Morkisz, Saeid Motiian, Kirill Motkov, Seungjun Nah, Yashraj Narang, Deepak Narayanan, Thabang Ngazimbi, Julian Ouyang, Shubham Pachori, David Page, Yatian Pang, Sehwi Park, Mahesh Patekar, Mostofa Patwary, Marco Pavone, Trung Pham, Wei Ping, Soha Pouya, Shrimai Prabhumoye, Varun Praveen, Delin Qu, Hesam Rabeti, Morteza Ramezanali, Marilyn Reeb, Xuanchi Ren, Kristen Rumley, Wojciech Rymer, Jun Saito, Yeongho Seol, John Shao, Piyush Shekdar, Tianwei Shen, Humphrey Shi, Min Shi, Stella Shi, Kevin Shih, Mohammad Shoeybi, Mateusz Sieniawski, Shuran Song, Alexander Sotelo, Amir Sotoodeh, Sunil Srinivasa, Vignesh Srinivasakumar, Bartosz Stefaniak, Rahul Heinrich Steiger, Shangkun Sun, Jiaxiang Tang, Shitao Tang, Yangyang Tang, Yue Tang, Tolou Tavakkoli, Kayley Ting, Krzysztof Tomala, Wei-Cheng Tseng, Jibin Varghese, Sergei Vasilev, Thomas Volk, Raju Wagwani, Roger Waleffe, Andrew Z. Wang, Boxiang Wang, Haoxiang Wang, Qiao Wang, Shihao Wang, Shijie Wang, Ting-Chun Wang, Yan Wang, Yu Wang, Rohit Watve, David Wehr, Fangyin Wei, Xinshuo Weng, Jay Zhangjie Wu, Kedi Wu, Hongchi Xia, Summer Xiao, Tianjun Xiao, Kevin Xie, Daguang Xu, Jiashu Xu, Mengyao Xu, Ruqing Xu, Xingqian Xu, Yao Xu, Dinghao Yang, Dong Yang, Hans Yang, Xiaodong Yang, Xuning Yang, Yichu Yang, Yurong You, Zhiding Yu, Hao Yuan, Simon Yuen, Xiaohui Zeng, Pengcuo Zeren, Cindy Zha, Haotian Zhang, Jenny Zhang, Jing Zhang, Liangkai Zhang, Paris Zhang, Shun Zhang, Xuanmeng Zhang, Zhizheng Zhang, Ann Zhao, Yilin Zhao, Yuliya Zhautouskaya, Charles Zhou, Fengzhe Zhou, Shilin Zhu, Yuke Zhu, Dima Zhylko, Artur Zolkowski

专题命中 音视频多模态 :全模态世界模型,统一语言图像视频音频动作

AI总结 提出基于统一混合Transformer架构的全模态世界模型Cosmos 3,联合处理语言、图像、视频、音频和动作序列,在理解和生成任务上达到新最优,为具身智能体提供可扩展的通用骨干。

2606.14702 2026-06-18 cs.CV 新提交 专题 90

OmniVideo-100K: A Dataset for Audio-Visual Reasoning through Structured Scripts and Evidence Chains

OmniVideo-100K:通过结构化脚本和证据链进行音视频推理的数据集

Xinyue Cai, Chaoyou Fu, Yi-Fan Zhang, Ran He, Caifeng Shan

专题命中 音视频多模态 :音视频推理数据集与问答

AI总结 提出OmniVideo-100K数据集,通过实体锚定视频脚本和线索引导的QA生成机制,解决音视频问答中跨段实体不一致和长时推理不足的问题,微调模型在多个基准上取得显著提升。

Comments Project page: https://github.com/MiG-NJU/OmniVideo-100K

2602.04796 2026-06-18 eess.AS cs.SD 版本更新 专题 90

LALM-as-a-Judge: Benchmarking Large Audio-Language Models for Safety Evaluation in Multi-Turn Spoken Dialogues

LALM-as-a-Judge:用于多轮口语对话安全评估的大型音频语言模型基准测试

Amir Ivry, Shinji Watanabe

专题命中 音视频多模态 :音频语言模型安全评估基准

AI总结 针对口语对话中社会不安全内容评估仍以文本为中心、忽略韵律和转录失败的问题,提出包含24000个多轮口语对话的开放基准,评估6种大型音频语言模型在文本、音频和多模态设置下的敏感性、严重性顺序特异性和轮次位置偏差,发现音频提供非词汇证据,多模态增益非普遍且存在多种模式。

Comments Accepted to ICML 2026

2601.13836 2026-06-18 cs.CL cs.CV cs.MM 版本更新 专题 90

FutureOmni: Evaluating Future Forecasting from Omni-Modal Context for Multimodal LLMs

FutureOmni:从全模态上下文中评估多模态大语言模型的未来预测能力

Qian Chen, Jinlan Fu, Changsong Li, Min Zhang, See-Kiong Ng, Xipeng Qiu

专题命中 音视频多模态 :评估多模态大模型从音视频预测未来的能力

AI总结 提出FutureOmni基准,评估多模态大模型从音视频线索预测未来的能力,发现现有模型在语音密集场景下表现差,并设计OFF训练策略提升性能。

Comments Accepted by ICML 2026

2606.19157 2026-06-18 eess.AS cs.CL 新提交 专题 85

IndicContextEval: A Benchmark for Evaluating Context Utilisation in Audio Large Language Models Across 8 Indic Languages

IndicContextEval:评估8种印度语言音频大语言模型上下文利用能力的基准

Sakshi Joshi, Dhruv Subhash Rathi, Sanskar Singh, Eldho Ittan George, R J Hari, Kaushal Bhogale, Mitesh M. Khapra

专题命中 音视频多模态 :评估音频大语言模型的上下文利用能力

AI总结 提出IndicContextEval基准,包含8种印度语言555位说话人的56小时自然语音,通过7级提示框架评估音频大语言模型是否真正利用上下文而非依赖参数化知识。

Comments Accepted at Interspeech 2026

2606.18924 2026-06-18 cs.SD 新提交 专题 85

Who Wins the Conflict? Mechanistic Interpretability of Text Bias in Audio LLMs

谁赢得冲突?音频大模型中文本偏差的机制可解释性

Hyebin Cho, Suho Yoo, Jaehyuk Jang, Changick Kim, Joon Son Chung

专题命中 音视频多模态 :分析音频大模型中文本偏差机制

AI总结 本文通过机制分析揭示音频大模型中的文本主导偏差,发现文本路径主动抑制完整音频表征,并提出无训练干预方法back-patching以增强音频表征,缓解文本主导。

Comments Preprint

2606.18273 2026-06-18 cs.CL cs.AI cs.SD eess.AS 新提交 专题 85

Continuous Audio Thinking for Large Audio Language Models

面向大型音频语言模型的连续音频思考

Gyojin Han, Dong-Jae Lee, Changho Choi, Jongsuk Kim, Junmo Kim

专题命中 音视频多模态 :提出CoAT框架,增强音频语言模型的连续音频思考能力。

AI总结 提出连续音频思考(CoAT)框架,通过专家蒸馏在连续潜在空间中组织声学信息,使音频语言模型在生成响应前利用丰富声学特征,无需额外自回归解码成本,在多个音频任务上提升性能。

Comments Preprint

2606.06170 2026-06-18 eess.AS 版本更新 专题 85

CoSTA: Cognitive-State-Conditioned TTS Data Augmentation Using ASR Transcripts for Alzheimer's Disease Detection

CoSTA: 基于认知状态条件的TTS数据增强,使用ASR转录文本用于阿尔茨海默病检测

Yin-Long Liu, Yuanchao Li, Yiming Wang, Yue Li, Rui Feng, Jiaxin Chen, Shaobo Liu, Liu He, Yuang Chen, Jiahong Yuan, Zhen-Hua Ling

专题命中 音视频多模态 :TTS与ASR结合的多模态数据增强用于AD检测

AI总结 提出CoSTA框架,通过认知状态条件TTS模型合成语音,结合ASR转录文本进行数据增强,在ADReSS数据集上实现85.83%的音频检测准确率。

Comments Accepted by Interspeech 2026

2605.26672 2026-06-18 cs.MM cs.SD 版本更新 专题 85

Can We Hear from Events? Generating Speech from Event Camera

我们能从事件中听到声音吗?从事件相机生成语音

Jingping Fang, Lin Chen, Chenyang Xu, Tong Zhao, Weidong Cai, Xiaoming Chen

专题命中 音视频多模态 :事件相机生成语音,跨模态语音生成

AI总结 提出EventSpeech框架,利用神经形态事件相机的高时间精度解决传统RGB语音生成中的时间粒度不匹配问题,实现情感丰富且抗运动模糊的语音生成。

2509.22363 2026-06-18 cs.LG eess.AS 版本更新 专题 85

Investigating Faithfulness in Large Audio Language Models

大型音频语言模型中的忠实性研究

Pooneh Mousavi, Lovenya Jain, Mirco Ravanelli, Cem Subakan

专题命中 音视频多模态 :评估大型音频语言模型的推理忠实性

AI总结 提出系统框架评估大型音频语言模型在推理链忠实性上的表现,定义三个音频忠实性标准,并通过基准测试发现模型推理与音频输入存在脱节。

Comments Accepted to Interspeech 2026

2603.10827 2026-06-18 cs.SD cs.AI 版本更新 专题 85

Speaker Verification with Speech-Aware LLMs: Evaluation and Augmentation

语音感知大语言模型的说话人验证:评估与增强

Thomas Thebaud, Yuzhe Wang, Laureano Moro-Velazquez, Jesus Villalba-Lopez, Najim Dehak

专题命中 音视频多模态 :语音感知大模型用于说话人验证

AI总结 提出模型无关的评分协议评估语音感知LLM的说话人区分能力(EER>20%),并通过注入冻结的ECAPA-TDNN说话人嵌入和LoRA微调,实现接近专用系统的性能(EER 1.03%)。

Comments 3 Tables, 1 Figure, Published in Interspeech 2026

2603.05128 2026-06-18 eess.AS cs.SD 版本更新 专题 85

PolyBench: A Benchmark for Compositional Reasoning in Polyphonic Audio

PolyBench:多声部音频中组合推理的基准测试

Yuanjian Chen, Yang Xiao, Han Yin, Xubo Liu, Jinjie Huang, Ting Dang

专题命中 音视频多模态 :多声部音频组合推理基准测试

AI总结 针对多声部音频中组合推理评估缺失的问题,提出PolyBench基准,包含计数、分类、检测、并发和时长估计五个子集,评估发现现有大音频语言模型在多声部场景下性能持续下降。

Comments Accepted by INTERSPEECH 2026

2606.19203 2026-06-18 eess.AS 新提交 专题 80

DASH: Dual-View Self-Distillation with Multi-Layer Hidden Representations for Robust Speech Recognition

DASH: 基于多层隐藏表示的双视角自蒸馏用于鲁棒语音识别

Jaeeun Baik, Ui-Hyeop Shin, Jiwoon Lee, Woocheol Jeong, Hyung-Min Park

专题命中 音视频多模态 :提出自蒸馏框架提升语音识别鲁棒性,属于音频处理

AI总结 提出DASH自蒸馏框架,通过双视角学习干净-噪声一致性,从多层编码器蒸馏隐藏表示并最小化原型分配分布的KL散度,在保持干净准确率的同时提升噪声鲁棒性,额外开销仅约微调时间的4%。

Comments Accepted to Interspeech 2026

2606.05739 2026-06-18 cs.SD eess.AS 版本更新 专题 80

Do speech foundation models perceive speaker similarity as humans do?

语音基础模型是否像人类一样感知说话人相似性?

Minoru Kishi, Hayato Yagi, Shinnosuke Takamichi, Yuki Saito

专题命中 音视频多模态 :语音基础模型说话人嵌入与人类感知比较

AI总结 本研究通过比较40多个语音基础模型的说话人嵌入与人类主观相似性评分,探究模型距离是否与人类感知一致,并识别影响模型与人类感知一致性的关键配置因素。

Comments Accepted by INTERSPEECH 2026. Camera-ready version

2603.09234 2026-06-18 eess.AS 版本更新 专题 70

StuPASE: Towards Low-Hallucination Studio-Quality Generative Speech Enhancement

StuPASE:迈向低幻觉、工作室质量的生成式语音增强

Xiaobin Rong, Jun Gao, Zheng Wang, Mansur Yesilbursa, Kamil Wojcicki, Jing Lu

专题命中 音视频多模态 :生成式语音增强,属于音频处理

AI总结 提出StuPASE,基于PASE框架,通过使用干目标微调和流匹配模块替代GAN,在保持低幻觉的同时实现工作室级语音质量,优于现有方法。

Comments Accepted to Interspeech 2026