OmniInteract: Benchmarking Real-World Streaming Interaction for Real-Time Omnimodal Assistants
We introduce OmniInteract, a streaming benchmark for real-time omnimodal large language models evaluated through native online inference over audio-visual streams. Unlike offline video understanding or text-prompted streaming QA, OmniInteract preserves the original audio-visual stream and requires models to process it online, without access to future content. User queries and ambient sounds are embedded in the audio track, requiring models to detect multimodal triggers, decide when to respond, and answer while the stream unfolds. OmniInteract contains 250 videos with 1,430 temporally grounded response slots: 1,062 1Q1A slots across real-time, proactive, and nested scenarios, and 368 1QnA ...
| Authors | Xudong Lu, Xueying Li, Annan Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 26 May 2026 |
| Last updated at source | 26 May 2026 |
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- lucky-lance/OmniInteractDataset
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