OmniInteract: Benchmarking Real-World Streaming Interaction for Real-Time Omnimodal Assistants
arXiv:2605.26485v1 Announce Type: cross Abstract: 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 c
The rapid advancement of large language models and multimodal AI necessitates new benchmarks to evaluate their real-world interactive capabilities, especially for real-time applications.
This benchmark addresses a critical gap in assessing omnimodal assistants, pushing towards more realistic and robust AI systems that can interact with complex, unfolding environments.
The focus shifts from offline or text-prompted interaction to continuous, real-time processing of audio-visual streams, demanding AI models to dynamically react and adapt.
- · AI model developers specializing in real-time omnimodal processing
- · Hardware manufacturers for edge AI and low-latency processing
- · Companies developing AI-powered virtual assistants
- · AI models reliant solely on offline or batch processing
- · Developers unprepared for real-time, continuous inference challenges
New research and development efforts will concentrate on online, real-time omnimodal AI architectures.
This could accelerate the deployment of highly interactive AI assistants in consumer devices, smart homes, and industrial settings.
The enhanced AI interaction capabilities may further blur the lines between human and AI communication, changing user expectations for digital interfaces.
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Read at arXiv cs.CL