1 citations · 1 across the 9 of their papers we have counts for
10 papers
Multimodal Duplex Interaction Agent
Orantqing, Shengpeng Ji, Junlong Tong +19
In this work, we present Gander, a native multimodal duplex interaction model that builds on MiniCPM-o 4.5 and is further adapted for realtime interaction with an asynchronous agen…
Audio-DeepThinker: Progressive Reasoning-Aware Reinforcement Learning for High-Quality Chain-of-Thought Emergence in Audio Language Models
Xiang He, Chenxing Li, Jinting Wang +5
Large Audio-Language Models (LALMs) have made significant progress in audio understanding, yet they primarily operate as perception-and-answer systems without explicit reasoning pr…
Covo-Audio Technical Report
Wenfu Wang, Chenxing Li, Liqiang Zhang +23
In this work, we present Covo-Audio, a 7B-parameter end-to-end LALM that directly processes continuous audio inputs and generates audio outputs within a single unified architecture…
SemanticVocoder: Bridging Audio Generation and Audio Understanding via Semantic Latents
Zeyu Xie, Chenxing Li, Qiao Jin +6
Recent audio generation models typically rely on Variational Autoencoders (VAEs) and perform generation within the VAE latent space. Although VAEs excel at compression and reconstr…
PhyAVBench: A Challenging Audio Physics-Sensitivity Benchmark for Physically Grounded Text-to-Audio-Video Generation
Tianxin Xie, Wentao Lei, Kai Jiang +27
Text-to-audio-video (T2AV) generation is central to applications such as filmmaking and world modeling. However, current models often fail to produce physically plausible sounds. P…
U-Codec: Ultra Low Frame-rate Neural Speech Codec for Fast High-fidelity Speech Generation
Xusheng Yang, Long Zhou, Wenfu Wang +6
We propose \textbf{U-Codec}, an \textbf{U}ltra low frame-rate neural speech \textbf{Codec} that achieves high-fidelity reconstruction and fast speech generation at an extremely low…
VCB Bench: An Evaluation Benchmark for Audio-Grounded Large Language Model Conversational Agents
Jiliang Hu, Wenfu Wang, Zuchao Li +6
Recent advances in large audio language models (LALMs) have greatly enhanced multimodal conversational systems. However, existing benchmarks remain limited -- they are mainly Engli…
When Audio Generators Become Good Listeners: Generative Features for Understanding Tasks
Zeyu Xie, Chenxing Li, Xuenan Xu +6
This work pioneers the utilization of generative features in enhancing audio understanding. Unlike conventional discriminative features that directly optimize posterior and thus em…
DegDiT: Controllable Audio Generation with Dynamic Event Graph Guided Diffusion Transformer
Yisu Liu, Chenxing Li, Wanqian Zhang +6
Controllable text-to-audio generation aims to synthesize audio from textual descriptions while satisfying user-specified constraints, including event types, temporal sequences, and…
Audio-Thinker: Guiding Audio Language Model When and How to Think via Reinforcement Learning
Shu Wu, Chenxing Li, Wenfu Wang +4
Recent advancements in large language models, multimodal large language models, and large audio language models (LALMs) have significantly improved their reasoning capabilities thr…