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| Auteurs principaux: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2602.09823 |
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| _version_ | 1866917344076365824 |
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| author | Wang, Wenfu Li, Chenxing Zhang, Liqiang Zhao, Yiyang Zou, Yuxiang Li, Hanzhao Cui, Mingyu Zhang, Hao Wei, Kun Xu, Le Huang, Zikang Xu, Jiajun Hu, Jiliang He, Xiang Xie, Zeyu Kang, Jiawen Chen, Youjun Yu, Meng Yu, Dong Chen, Rilin Di, Linlin Feng, Shulin Hu, Na Liu, Yang Wang, Bang Yang, Shan |
| author_facet | Wang, Wenfu Li, Chenxing Zhang, Liqiang Zhao, Yiyang Zou, Yuxiang Li, Hanzhao Cui, Mingyu Zhang, Hao Wei, Kun Xu, Le Huang, Zikang Xu, Jiajun Hu, Jiliang He, Xiang Xie, Zeyu Kang, Jiawen Chen, Youjun Yu, Meng Yu, Dong Chen, Rilin Di, Linlin Feng, Shulin Hu, Na Liu, Yang Wang, Bang Yang, Shan |
| contents | 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. Through large-scale curated pretraining and targeted post-training, Covo-Audio achieves state-of-the-art or competitive performance among models of comparable scale across a broad spectrum of tasks, including speech-text modeling, spoken dialogue, speech understanding, audio understanding, and full-duplex voice interaction. Extensive evaluations demonstrate that the pretrained foundation model exhibits strong speech-text comprehension and semantic reasoning capabilities on multiple benchmarks, outperforming representative open-source models of comparable scale. Furthermore, Covo-Audio-Chat, the dialogue-oriented variant, demonstrates strong spoken conversational abilities, including understanding, contextual reasoning, instruction following, and generating contextually appropriate and empathetic responses, validating its applicability to real-world conversational assistant scenarios. Covo-Audio-Chat-FD, the evolved full-duplex model, achieves substantially superior performance on both spoken dialogue capabilities and full-duplex interaction behaviors, demonstrating its competence in practical robustness. To mitigate the high cost of deploying end-to-end LALMs for natural conversational systems, we propose an intelligence-speaker decoupling strategy that separates dialogue intelligence from voice rendering, enabling flexible voice customization with minimal text-to-speech (TTS) data while preserving dialogue performance. Overall, our results highlight the strong potential of 7B-scale models to integrate sophisticated audio intelligence with high-level semantic reasoning, and suggest a scalable path toward more capable and versatile LALMs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_09823 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Covo-Audio Technical Report Wang, Wenfu Li, Chenxing Zhang, Liqiang Zhao, Yiyang Zou, Yuxiang Li, Hanzhao Cui, Mingyu Zhang, Hao Wei, Kun Xu, Le Huang, Zikang Xu, Jiajun Hu, Jiliang He, Xiang Xie, Zeyu Kang, Jiawen Chen, Youjun Yu, Meng Yu, Dong Chen, Rilin Di, Linlin Feng, Shulin Hu, Na Liu, Yang Wang, Bang Yang, Shan Sound Computation and Language Audio and Speech Processing 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. Through large-scale curated pretraining and targeted post-training, Covo-Audio achieves state-of-the-art or competitive performance among models of comparable scale across a broad spectrum of tasks, including speech-text modeling, spoken dialogue, speech understanding, audio understanding, and full-duplex voice interaction. Extensive evaluations demonstrate that the pretrained foundation model exhibits strong speech-text comprehension and semantic reasoning capabilities on multiple benchmarks, outperforming representative open-source models of comparable scale. Furthermore, Covo-Audio-Chat, the dialogue-oriented variant, demonstrates strong spoken conversational abilities, including understanding, contextual reasoning, instruction following, and generating contextually appropriate and empathetic responses, validating its applicability to real-world conversational assistant scenarios. Covo-Audio-Chat-FD, the evolved full-duplex model, achieves substantially superior performance on both spoken dialogue capabilities and full-duplex interaction behaviors, demonstrating its competence in practical robustness. To mitigate the high cost of deploying end-to-end LALMs for natural conversational systems, we propose an intelligence-speaker decoupling strategy that separates dialogue intelligence from voice rendering, enabling flexible voice customization with minimal text-to-speech (TTS) data while preserving dialogue performance. Overall, our results highlight the strong potential of 7B-scale models to integrate sophisticated audio intelligence with high-level semantic reasoning, and suggest a scalable path toward more capable and versatile LALMs. |
| title | Covo-Audio Technical Report |
| topic | Sound Computation and Language Audio and Speech Processing |
| url | https://arxiv.org/abs/2602.09823 |