DIVE: Benchmarking Full-Duplex Interaction with Visual Engagement in Continuous Streams
To address this gap, we introduce DIVE, a benchmark for evaluating full-duplex interaction with visual engagement under continuous and realistic multimodal streams.
ProofPaper ↗
Key points
- 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.
- (3) Interaction Coordination evaluates how models coordinate listening and speaking under overlapping and multi-party interactions.
Sources (1)
- [1]DIVE: Benchmarking Full-Duplex Interaction with Visual Engagement in Continuous StreamsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 03:48 PM
To address this gap, we introduce DIVE, a benchmark for evaluating full-duplex interaction with visual engagement under continuous and realistic multimodal streams.
Recent advances in multimodal large language models (MLLMs) have substantially improved spoken interaction and video understanding.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 20, 2026Voxtral 3
- Oct 20, 2026[AINews] DeepSeek DeepInfer: Open Source SOTA Inference running 155T toks/day/cluster making $22m/day at ~80% margin for a 81 day payback
- Oct 20, 2026Frontier LLMs Enter the World of Robotics: A Cross-Model Evaluation on RoboColiseum
- Oct 19, 2026Introducing Clairic, a new System One class of extraction models
- Oct 19, 2026NVIDIA Cosmos3-H-Dreams: Bringing Clinical Surgery to Life Through Generative Simulation
- Oct 19, 2026Ropedia W0: Scaling Loco-Manipulation World Models across Embodiments with Human Experience