Discrete vs. Continuous: A Comprehensive Study of Unified Audio Understanding in LALMs
Large Audio Language Models (LALMs) utilize either continuous features or discrete tokens, yet the optimal representation paradigm for general audio understanding remains debated. Existing benchmarks often focus on narrow domains or evaluate encoders outside LALM contexts. To address these gaps, we systematically evaluate continuous and discrete representations across speech, sound and music. Utilizing our UniARC framework with dual evaluation strategies across model scales from SmolLM2-135M to Llama-3-8B, we analyze the dynamic relationships of data volume, model capacity, and computational efficiency. Our results reveal the pivotal role of semantic constraints in tokenization for audio ...
| Authors | Jing Peng, Zichao Nie, Zhisheng Zhang et al. |
|---|---|
| Primary category | cs.SD |
| Published | 19 September 2026 |
| Last updated at source | 22 September 2026 |
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