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[2602.13954] Eureka-Audio: Triggering Audio Intelligence in Compact Language Models
Summary
Eureka-Audio presents a compact audio language model that outperforms larger models in various audio understanding tasks, showcasing efficiency and strong performance.
Why It Matters
As audio intelligence becomes increasingly relevant in AI applications, Eureka-Audio's ability to deliver high performance with fewer parameters is significant. It addresses the need for efficient models in resource-constrained environments, making advanced audio processing more accessible.
Key Takeaways
- Eureka-Audio achieves competitive performance with only 1.7B parameters.
- The model excels in automatic speech recognition and audio understanding tasks.
- It utilizes a unique architecture combining a lightweight backbone and a Whisper-based audio encoder.
- DataFlux enhances the model's reasoning capabilities through high-quality data synthesis.
- Eureka-Audio sets a new baseline for lightweight audio understanding models.
Computer Science > Sound arXiv:2602.13954 (cs) [Submitted on 15 Feb 2026] Title:Eureka-Audio: Triggering Audio Intelligence in Compact Language Models Authors:Dan Zhang, Yishu Lei, Jing Hu, Shuwei He, Songhe Deng, Xianlong Luo, Danxiang Zhu, Shikun Feng, Rui Liu, Jingzhou He, Yu Sun, Hua Wu, Haifeng Wang View a PDF of the paper titled Eureka-Audio: Triggering Audio Intelligence in Compact Language Models, by Dan Zhang and 12 other authors View PDF HTML (experimental) Abstract:We present Eureka-Audio, a compact yet high-performance audio language model that achieves competitive performance against models that are 4 to 18 times larger across a broad range of audio understanding benchmarks. Despite containing only 1.7B parameters, Eureka-Audio demonstrates strong performance on automatic speech recognition (ASR), audio understanding, and dense audio captioning, matching or surpassing multiple 7B to 30B audio and omni-modal baselines. The model adopts a unified end-to-end architecture composed of a lightweight language backbone, a Whisper-based audio encoder, and a sparsely activated Mixture-of-Experts (MoE) adapter that explicitly accounts for audio heterogeneity and alleviates cross-modal optimization conflicts under limited capacity. To further enhance paralinguistic reasoning, we introduce DataFlux, a closed loop audio instruction data synthesis and verification pipeline that constructs high quality, logically consistent supervision from raw audio. Extensive evaluations ac...
