AudioRAG: A Challenging Benchmark for Audio Reasoning and Information Retrieval
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866910018879619072 |
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| author | Lin, Jingru Zhang, Chen Wang, Tianrui Li, Haizhou |
| author_facet | Lin, Jingru Zhang, Chen Wang, Tianrui Li, Haizhou |
| contents | Due to recent advancements in Large Audio-Language Models (LALMs) that demonstrate remarkable performance across a range of sound-, speech- and music-related tasks, there is a growing interest in proposing benchmarks to assess these models. Existing benchmarks generally focus only on reasoning with internal knowledge, neglecting real-world scenarios that require external information grounding. To bridge this gap, we introduce AudioRAG, a novel benchmark designed to evaluate audio-based reasoning augmented by information retrieval in realistic web environments. This benchmark comprises both LLM-generated and manually curated question-answer pairs. Our evaluations reveal that even the state-of-the-art LALMs struggle to answer these questions. We therefore propose an agentic pipeline that integrates audio reasoning with retrieval-augmented generation, providing a stronger baseline for future research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_10656 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AudioRAG: A Challenging Benchmark for Audio Reasoning and Information Retrieval Lin, Jingru Zhang, Chen Wang, Tianrui Li, Haizhou Audio and Speech Processing Sound Due to recent advancements in Large Audio-Language Models (LALMs) that demonstrate remarkable performance across a range of sound-, speech- and music-related tasks, there is a growing interest in proposing benchmarks to assess these models. Existing benchmarks generally focus only on reasoning with internal knowledge, neglecting real-world scenarios that require external information grounding. To bridge this gap, we introduce AudioRAG, a novel benchmark designed to evaluate audio-based reasoning augmented by information retrieval in realistic web environments. This benchmark comprises both LLM-generated and manually curated question-answer pairs. Our evaluations reveal that even the state-of-the-art LALMs struggle to answer these questions. We therefore propose an agentic pipeline that integrates audio reasoning with retrieval-augmented generation, providing a stronger baseline for future research. |
| title | AudioRAG: A Challenging Benchmark for Audio Reasoning and Information Retrieval |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2602.10656 |