TSR Desk · physics · 6 October 2026, 01:00 UTC
EXAM2: Extending Audio Understanding in Multilingual and Multimodal Analysis
- What
- EXAM2: Extending Audio Understanding in Multilingual and Multimodal Analysis
- Who
- arxiv.org
- When
- 5 October 2026, 04:00 UTC
- Category
- Physics
- Primary source
- https://arxiv.org/abs/2608.23758
- What is not known
- This brief does not claim independent replication. Claims that appear only on X and not in the primary source stay unknown.
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. It comes from a paper posted to arXiv on 5 October 2026. 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. Furthermore, we propose Gemma3n-EXAM$^2$, a lightweight fusion-model fine-tuned on EXAM$^2$-train, achieves up to $15.8\%$ improvement in multilingual settings and $16.5\%$ 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.
Why it counts
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 $15.8\%$ improvement in multilingual settings and $16.5\%$ gains in multimodal evaluation over a strong baseline.
Sources
Primary source: primary source
What is not known
This brief does not claim independent replication. Claims that appear only on X and not in the primary source stay unknown.
No clip. The article still stands.