Vox-Infinity: Benchmarking the Limits of Long-Context Spoken Language Models
Long-context understanding remains a fundamental challenge for large language models, as excessively long inputs often lead models to forget salient information. This issue is even more pronounced in the speech domain, where audio, as a low-compression modality, requires substantially more embeddings than text to preserve both semantic content and acoustic cues. To address this challenge, we introduce \textbf{Vox-Infinity}, the first benchmark specifically designed to evaluate long-context understanding in spoken language models. Vox-Infinity systematically extends audio history along two dimensions: turn count and turn duration. It covers a diverse range of representative scenarios with ...
| Authors | Xize Cheng, Wenxu Jia, Chenyuhao Wen et al. |
|---|---|
| Primary category | cs.CL |
| Published | 18 September 2026 |
| Last updated at source | 18 September 2026 |
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