60 citations · 127 across the 20 of their papers we have counts for
24 papers
Beyond Watching and Searching: Benchmarking Agents for Video-centered Deep Research Reports
Jiahao Meng, Qize Yang, Yuxuan Wang +4
Deep research agents synthesize open-web evidence into long reports, but current benchmarks do not evaluate research centered on a source video. We introduce Video-Centered Deep Re…
VisionWeave: Weaving Elastic Visual Representations as a Native Capability of MLLMs
Yuan Feng, Qize Yang, Ruizhe Chen +9
Multimodal large language models have become the dominant paradigm for visual understanding, but incur substantial costs by encoding inputs into dense, fixed-size patch tokens. How…
MMPostTrainBench: Benchmarking Autonomous Research for Multimodal Post-Training
Yuxin Liu, Yuxuan Wang, Zhenxin Lei +9
Autonomous research seeks sustained model improvements through iterative experimentation and feedback. LLM agents show promise in automating machine learning and language-model pos…
OmniReasoning: Pushing the Limits of Audio-Visual Joint Reasoning
Junming Lin, Yuxuan Wang, Zhenxin Lei +11
Recent advances have enabled unified omni-modal models in understanding audio, vision, and language. However, existing benchmarks, training data, and learning methods largely treat…
OmniEcho: Audio-Visual Spatial Understanding for Omni-Modal Embodied Agents
Ruixun Liu, Yuxuan Wang, Jiacheng Xie +10
Humans can effortlessly localize the direction of a sound source and integrate it with visual cues for reasoning, yet this remains challenging for embodied agents. In particular, i…
Omni2Web: Benchmarking Audiovisual Website Development
Minghao Han, Zhenghao Xing, Xize Cheng +7
Screen-recorded web editing requests contain weak deictic expressions such as ``this'' and ``there,'' whose referents depend on speech, cursor trajectories, page state, and edit hi…
OmniVChat: Synthesizing, Benchmarking, and Training for Native Audio-Visual Dialogue
Haolin He, Yunfei Chu, Qi Chen +15
We define OmniVChat (Omni Video Chat) as the task of native audio-visual dialogue between a user and an omni model. In OmniVChat, omni models directly and simultaneously receive au…
Omni Demand Understanding: A Benchmark for Contextual User-Intent Inference in Multimodal Interaction
Qi Chen, Yunfei Chu, Haolin He +15
Natural audio-visual interaction is emerging as an important interface for AI assistants, allowing users to communicate through speech and vision rather than carefully composed tex…
LongAudioSpan: Spanning the Duration and Depth of Audio Comprehension
Wen Huang, Yunfei Chu, Meng Gao +2
General audio comprehension now covers speech, sound, and music over durations from seconds to hours, driven by large audio-language models (LALMs) that are increasingly omni-modal…
Qwen-Music Technical Report
Jin Xu, Kangdi Wang, Ruibin Yuan +24
We introduce Qwen-Music, a music generation model that produces high-fidelity songs with complete vocals. It supports text-to-music generation from descriptions, lyrics, and musica…
Native Active Perception as Reasoning for Omni-Modal Understanding
Zhenghao Xing, Ruiyang Xu, Yuxuan Wang +8
Passive models for long video understanding typically rely on a "watch-it-all" paradigm, processing frames uniformly regardless of query difficulty, causing computational cost to g…
AstroMMBench: A Benchmark for Evaluating Multimodal Large Language Models Capabilities in Astronomy
Jinghang Shi, Xiaoyu Tang, Yang Huang +4
Astronomical image interpretation presents a significant challenge for applying multimodal large language models (MLLMs) to specialized scientific tasks. Existing benchmarks focus…
Omni-Captioner: Data Pipeline, Models, and Benchmark for Omni Detailed Perception
Ziyang Ma, Ruiyang Xu, Zhenghao Xing +9
Fine-grained perception of multimodal information is critical for advancing human-AI interaction. With recent progress in audio-visual technologies, Omni Language Models (OLMs), ca…
Qwen3-Omni Technical Report
Jin Xu, Zhifang Guo, Hangrui Hu +35
We present Qwen3-Omni, a single multimodal model that, for the first time, maintains state-of-the-art performance across text, image, audio, and video without any degradation relat…
Qwen2.5-Omni Technical Report
Jin Xu, Zhifang Guo, Jinzheng He +11
In this report, we present Qwen2.5-Omni, an end-to-end multimodal model designed to perceive diverse modalities, including text, images, audio, and video, while simultaneously gene…
WavChat: A Survey of Spoken Dialogue Models
Shengpeng Ji, Yifu Chen, Minghui Fang +16
Recent advancements in spoken dialogue models, exemplified by systems like GPT-4o, have captured significant attention in the speech domain. Compared to traditional three-tier casc…
Analyzing and Mitigating Inconsistency in Discrete Audio Tokens for Neural Codec Language Models
Wenrui Liu, Zhifang Guo, Jin Xu +4
Building upon advancements in Large Language Models (LLMs), the field of audio processing has seen increased interest in training audio generation tasks with discrete audio token s…
A Practice of Post-Training on Llama-3 70B with Optimal Selection of Additional Language Mixture Ratio
Ningyuan Xi, Yetao Wu, Kun Fan +3
Large Language Models (LLM) often need to be Continual Pre-Trained (CPT) to obtain unfamiliar language skills or adapt to new domains. The huge training cost of CPT often asks for…
Qwen2-Audio Technical Report
Yunfei Chu, Jin Xu, Qian Yang +9
We introduce the latest progress of Qwen-Audio, a large-scale audio-language model called Qwen2-Audio, which is capable of accepting various audio signal inputs and performing audi…
Qwen2 Technical Report
An Yang, Baosong Yang, Binyuan Hui +59
This report introduces the Qwen2 series, the latest addition to our large language models and large multimodal models. We release a comprehensive suite of foundational and instruct…
AIR-Bench: Benchmarking Large Audio-Language Models via Generative Comprehension
Qian Yang, Jin Xu, Wenrui Liu +8
Recently, instruction-following audio-language models have received broad attention for human-audio interaction. However, the absence of benchmarks capable of evaluating audio-cent…
An Adaptive Framework of Geographical Group-Specific Network on O2O Recommendation
Luo Ji, Jiayu Mao, Hailong Shi +3
Online to offline recommendation strongly correlates with the user and service's spatiotemporal information, therefore calling for a higher degree of model personalization. The tra…
Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models
Yunfei Chu, Jin Xu, Xiaohuan Zhou +5
Recently, instruction-following audio-language models have received broad attention for audio interaction with humans. However, the absence of pre-trained audio models capable of h…
LauraGPT: Listen, Attend, Understand, and Regenerate Audio with GPT
Zhihao Du, Jiaming Wang, Qian Chen +12
Generative Pre-trained Transformer (GPT) models have achieved remarkable performance on various natural language processing tasks, and have shown great potential as backbones for a…