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DIVE: Benchmarking Full-Duplex Interaction with Visual Engagement in Continuous Streams

arXiv · AI, language, vision and robotics · article · Oct 20, 2026 · UTC

Recent advances in multimodal large language models (MLLMs) have substantially improved spoken interaction and video understanding. Existing online and streaming benchmarks primarily assess causal multimodal perception through timestamped question answering, whereas real-world interaction requires models to continuously identify task-relevant multimodal cues and decide whether, when, and how to respond. This leaves open how well current models can sustain real-time interaction throughout an evolving multimodal stream. To address this gap, we introduce DIVE, a benchmark for evaluating full-dupl

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