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New OmniAssistBench benchmark reveals Omni-LLMs struggle with real-time video assistance

By PulseAugur Editorial · [2 sources] ·

A new benchmark, OmniAssistBench, has been developed to evaluate the performance of omni-modal large language models (Omni-LLMs) as real-time video assistants. The benchmark, constructed by reverse-engineering internet videos, revealed that current models struggle with visual prompts, maintaining context over multiple turns, and responding in a timely manner. In evaluations, Google's Gemini 3-Pro scored 66.4, while the open-source Qwen3-Omni-Instruct achieved 51.2, indicating significant room for improvement before these models can reliably function as interactive assistants. AI

IMPACT This benchmark highlights critical areas for improvement in omni-modal LLMs, particularly in visual understanding and contextual interaction, which are key for developing effective AI assistants.

RANK_REASON The cluster describes the release of a new academic benchmark for evaluating omni-modal LLMs.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New OmniAssistBench benchmark reveals Omni-LLMs struggle with real-time video assistance

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The cluster describes the release of a new academic benchmark for evaluating omni-modal LLMs.
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COVERAGE [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    OmniAssistBench: Assistant-style Interaction Benchmark for Omni-LLMs

    OmniAssistBench evaluates real-time interactive video assistants by reverse-engineering multi-turn interaction videos, revealing that current omni-modal models struggle with visual prompts, context retention, and timely responses.

  2. arXiv cs.CV TIER_1 English(EN) · Xianyun Sun, Chaoyou Fu, Zhengye Zhang, Feiyang Duan, Qingyuan Cao, Yonghui Niu, Sihang Yuan, Ge Zhang, Caifeng Shan ·

    OmniAssistBench: Assistant-style Interaction Benchmark for Omni-LLMs

    arXiv:2608.21360v1 Announce Type: new Abstract: Recent omni-modal large language models (Omni-LLMs) show great potential as real-time video assistants, which continuously perceive environments and guide users to achieve specific goals. Unlike traditional passive video understandi…

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