{"ID":9739156,"CreatedAt":"2026-08-26T02:26:19.576451262Z","UpdatedAt":"2026-08-27T08:34:41.278007069Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2608.23758","arxiv_id":"2608.23758","title":"EXAM$^2$: $\\underline{Ex}tending$ $\\underline{A}udio$ $Understanding$ $in$ $\\underline{M}ultilingual$ $and$ $\\underline{M}ultimodal$ $Analysis$","abstract":"Recent large audio language models (LALMs) have achieved impressive progress in audio understanding. However, existing evaluations remain largely constrained to English and narrow audio domains. Prior benchmarks typically focus on a single audio modality, i.e., speech, sound, or music, limiting the systematic investigation into how these models generalize across diverse visual scenarios. In this paper, we introduce EXAM$^2$, a benchmark for multilingual and multimodal audio understanding spanning six languages and multiple modalities, including speech, sound, music, mixed-audio settings, and visual images. By incorporating visual information alongside heterogeneous audio inputs, EXAM$^2$ enables more realistic evaluation of scene-aware audio reasoning and cross-modal comprehension. EXAM$^2$ comprises $5,667$ multiple-choice questions, $22,614$ image instances, and $135,684$ multilingual translations. We evaluate state-of-the-art open-source and proprietary LALMs as well as multimodal LLMs, revealing substantial performance gaps in multilingual and cross-modal understanding. Furthermore, we propose Gemma3n-EXAM$^2$, a lightweight fusion-model fine-tuned on EXAM$^2$-train, achieves up to $12.4\\%$ improvement in multilingual settings and $21.7\\%$ gains in multimodal evaluation over a strong baseline. Empirical results establish EXAM$^2$ as a challenging benchmark and pioneer future multilingual and multimodal audio intelligence research.","short_abstract":"Recent large audio language models (LALMs) have achieved impressive progress in audio understanding. However, existing evaluations remain largely constrained to English and narrow audio domains. Prior benchmarks typically focus on a single audio modality, i.e., speech, sound, or music, limiting the systematic investiga...","url_abs":"https://arxiv.org/abs/2608.23758","url_pdf":"https://arxiv.org/pdf/2608.23758v1","authors":"[\"Jiawen Wang\",\"Xiaoxue Gao\",\"Zi Haur Pang\",\"Nancy F. Chen\"]","published":"2026-08-24T18:52:22Z","proceeding":"cs.SD","tasks":"[\"cs.SD\",\"cs.AI\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}