Qwen-Audio-Jev: A Unified Interface for Audio Decisions
Large audio language models (LALMs) have made substantial progress in audio understanding. However, in high-throughput applications requiring frequent selection, judgment, and scoring, autoregressive text generation introduces additional computational overhead and response latency. To address this limitation, we introduce Qwen-Audio-Jev by augmenting Qwen-Audio-3.1-ASR-Flash-Next with a dedicated Jev decision head. The model unifies selection, binary judgment, and scoring through three decision primitives: CHOICE, NOUL, and SCORE. Given audio, a natural-language question, and an explicit answer specification, it directly produces a task-conditioned probability distribution or a numerical ...
| Authors | Ying Fang, Zhifu Gao, Qing Gu et al. |
|---|---|
| Primary category | cs.SD |
| Published | 15 October 2026 |
| Last updated at source | 15 October 2026 |
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