activity
20242026
most citedQwen2 Technical Report

60 citations · 82 across the 10 of their papers we have counts for

collaborators

10 papers

cs.CV2026

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…

cs.CL2025★ 1 cited

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…

cs.CL2025★ 2 cited

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…

cs.CL2025

InvestAlign: Overcoming Data Scarcity in Aligning Large Language Models with Investor Decision-Making Processes under Herd Behavior

Huisheng Wang, Zhuoshi Pan, Hangjing Zhang +3

Aligning Large Language Models (LLMs) with investor decision-making processes under herd behavior is a critical challenge in behavioral finance, which grapples with a fundamental l…

eess.AS2025

WavReward: Spoken Dialogue Models With Generalist Reward Evaluators

Shengpeng Ji, Tianle Liang, Yangzhuo Li +11

End-to-end spoken dialogue models such as GPT-4o-audio have recently garnered significant attention in the speech domain. However, the evaluation of spoken dialogue models' convers…

cs.CL2025★ 12 cited

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…

cs.SD2025

WavRAG: Audio-Integrated Retrieval Augmented Generation for Spoken Dialogue Models

Yifu Chen, Shengpeng Ji, Haoxiao Wang +5

Retrieval Augmented Generation (RAG) has gained widespread adoption owing to its capacity to empower large language models (LLMs) to integrate external knowledge. However, existing…

cs.SD2024

SongTrans: An unified song transcription and alignment method for lyrics and notes

Siwei Wu, Jinzheng He, Ruibin Yuan +5

The quantity of processed data is crucial for advancing the field of singing voice synthesis. While there are tools available for lyric or note transcription tasks, they all need p…

eess.AS2024★ 7 cited

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…

cs.CL2024★ 60 cited

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…