Qwen-Audio-Jev: A Unified Interface for Audio Decisions
To address this limitation, we introduce Qwen-Audio-Jev by augmenting Qwen-Audio-3.1-ASR-Flash-Next with a dedicated Jev decision head.
ProofPaper ↗
Key points
- 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.
- Given audio, a natural-language question, and an explicit answer specification, it directly produces a task-conditioned probability distribution or a numerical score through typed decision readouts.
- It demonstrates its potential for efficient, high-throughput audio decision-making in applications such as large-scale audio screening, speaker verification, and automated quality assessment.
Sources (1)
- [1]Qwen-Audio-Jev: A Unified Interface for Audio DecisionsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 09:59 AM
To address this limitation, we introduce Qwen-Audio-Jev by augmenting Qwen-Audio-3.1-ASR-Flash-Next with a dedicated Jev decision head.
Large audio language models (LALMs) have made substantial progress in audio understanding.
Extractive summary: sentences quoted from the sources.
Before this
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