Large audio language models (LALMs) can describe what is heard, but their ability to localize when queried content occurs remains less systematically evaluated. We present TAG-Bench, a benchmark for temporal audio grounding in which a model returns every time interval that matches a natural-language query. TAG-Bench co...
Yu-Hang Dai, Xin Shu, Zeng-Xi Li et al.· 2 citations
Universal audio representations must preserve acoustic detail while making high-level concepts accessible across speech, music, environmental sound, and downstream models of different capacities. We study semantic refinement of an acoustically pretrained encoder by adding audio-description alignment to a foundation of...
Le-Jun Min, Jun-Yu Dai, Rui-Chen Zheng et al.· 0 citations
Qwen-Audio-3.0-Gen-Preview is presented, a unified non-autoregressive framework that uses a Diffusion Transformer and a shared variational autoencoder (VAE) to generate the complete mixed waveform and demonstrates the potential of unified generation for temporally structured audio without task-specific branches.
Jun-Yu Dai, Xiao-Yue Duan, Xin-Yu Fan et al.· 2 citations
A joint GDPO task in which the model learns when to use a clue and how to produce a speaker-attributed transcript from separately normalized format, gate, and transcript rewards is formulated.
The Platonic brain bridge hypothesis is proposed: omni models, multimodal large language models that process video, audio and text jointly, converge on brain-like representations usable in both directions, and human brain organization is a usable architectural prior for multimodal large language models.
Peng-Fei Zhang, Biao Tian, Xian-Gang Li et al.· 0 citations
Qwen-Audio-3.0-ASR is presented, a Mixture-of-Experts (MoE) LLM-based ASR system designed to address production demands through a unified, instruction-following framework and demonstrates state-of-the-art or highly competitive recognition performance across a broad range of evaluation conditions.
Chuan-Meng Bian, Da-Ren Chen, Pei-Xin Chen et al.· 3 citations
This work develops a new reinforcement learning signal named ConsensusPR, which directly reduces the reward sparsity of outcome reward across long reasoning trajectories, and introduces ConsensusBench, a novel dataset designed to provide rule-based process-level signals.
Shi-Qi Yan, Chao-Hong Tan, Qian Chen et al.· 0 citations
FullDiT is introduced, a conditional DiT that fuses eight frame-aligned RVQ streams with independently encoded captions and lyrics and uses non-causal self-attention over the complete acoustic latent sequence and outperforms five commercial systems on 15 of 18 automatic metrics.
Yun-Jia Li, Meng-Li Wu, Jun-Yu Dai et al.· 0 citations
In this report, we present Qwen-Audio-3.0-TTS, a production-oriented speech synthesis system that jointly advances content consistency, speaker similarity, prosodic naturalness, audio quality, controllability, multilingual coverage, efficiency, and robustness. It combines a 12.5~Hz low-frame-rate speech tokenizer for r...
Bajian Xiang, Cheng Wen, Han Zhao et al.· 5 citations· ⚡1
This work proposes Spoken Function Calling (SFC), a novel semantic understanding perspective that optimizes semantic understanding with structured rule definitions, to evolve beyond traditional closed-set SLU.
Yue-Zhang Peng, Yuxin Liu, Changfeng Gao et al.· 1 citation
This work proposes Efficient Chain-of-Modality Reasoning (ECoM Reasoning), the first framework to introduce compressed reasoning into SLMs, and proposes Progressive Compression, a curriculum-based strategy that gradually trains the model from full-form reasoning to compressed reasoning.
Pengchao Feng, Chao-Hong Tan, Qian Chen et al.· ACM International Conference...· 0 citations
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