Omni-DeepSearch: A Benchmark for Audio-Driven Omni-Modal Deep Search
Current omni-modal benchmarks mainly evaluate models under settings where multiple modalities are provided simultaneously, while the ability to start from audio alone and actively search for cross-modal evidence remains underexplored. In this paper, we introduce \textbf{Omni-DeepSearch}, a benchmark for audio-driven omni-modal deep search. Given one or more audio clips and a related question, models must infer useful clues from audio, invoke text, image, and video search tools, and perform multi-hop reasoning to produce a short, objective, and verifiable answer. Omni-DeepSearch contains 640 samples across 15 fine-grained categories, covering four retrieval target modalities and four audio...
| Authors | Tao Yu, yiming ding, Shenghua Chai et al. |
|---|---|
| Primary category | cs.SD |
| Published | 9 May 2026 |
| Last updated at source | 9 May 2026 |
Models and datasets referencing this paper
- Kirito-Lab/Omni-DeepSearchDataset
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