Vox-Infinity: Benchmarking the Limits of Long-Context Spoken Language Models

2026-09-18

AI Summary

本文通过引入Vox-Infinity基准来解决长上下文语音模型理解能力不足的问题，该基准通过增加对话轮数和时长评估模型处理长音频输入的能力。

Abstract

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 varying interaction structures and semantic complexity. Crucially, Vox-Infinity provides explicit answer-provenance annotations and organizes samples according to the amount of historical context required to resolve each query, enabling precise and length-aware evaluation. Extensive evaluations of seven representative spoken language models reveal a clear overall recency effect: models generally achieve higher accuracy when answer-supporting evidence is closer to the query, but struggle to retrieve and use evidence located farther back in the dialogue history. Cases and datasets are available at https://vox-infinity.github.io.

Problem

• long-context understanding
• spoken language models
• audio embeddings
• semantic content
• acoustic cues

Innovation

• Vox-Infinity
• long-context understanding
• spoken language models
• audio history
• answer-provenance annotations