Omnimodal large language models (Omni-LLMs) are increasingly expected to assist users while observing continuous audiovisual streams. However, existing evaluations provide limited insight into how rel
Omni-InteractScope evaluates continuous audiovisual interaction reliability, revealing that current Omni-LLMs struggle with timely tool use, query-free reminders, and sustained guidance.
Omnimodal large language models (Omni-LLMs) are increasingly expected to assist users while observing continuous audiovisual streams. However, existing evaluations provide limited insight into how reliably these models connect stream evidence and user needs to timely, sustained interventions. We introduce Omni-InteractScope, a benchmark of 1,479 human-annotated, cross-reviewed interactions with 1,790 temporal targets across eight task groups. It links input modalities, event triggers, intervention types, and user needs, covering event-timed tool invocation and query-free assistance alongside notifications, warnings, state tracking, and continuous responses. Our experimental results on the…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
Omni-InteractScope: Benchmarking Proactive and Agentic Interaction in Continuous Omnimodal Streams was first seen on Hugging Face · Daily papers on Oct 28, 2026. Since then it has appeared in 21 checks on 2 different days. Its best position was #21 of 30, reached on Oct 29, 21:28 UTC. It dropped off the list after Oct 29, 21:28 UTC.
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Not at the moment. It was last on the list on Oct 29, 21:28 UTC, after 2 days of trending.
#21 on Hugging Face · Daily papers, on Oct 29, 21:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Oct 28, 2026 and has been on the list on 2 days (21 checks) since.