OmniAssistBench: Assistant-style Interaction Benchmark for Omni-LLMs
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 understanding, interactive assistants should actively combine visual states, user goals, and prior knowledge to provide effective help. Evaluating this is rather challenging, as the model's unpredictable response dynamically changes the user's subsequent actions, which static offline datasets cannot accommodate. To address this bottleneck, we introduce OmniAssistBench. To solv
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- FuzzyOverlapping authors or contributors · 62%google-research/google-research →
“Shared author/contributor keys: sun”
- FuzzySimilar title/name (fuzzy) · 59%jeinlee1991/chinese-llm-benchmark →
“Fuzzy title match (0.73): “OmniAssistBench: Assistant-style Interaction Benchmark for O” ≈ “jeinlee1991/chinese-llm-benchmark””
- LinkedLinked via arxiv author · 85%Xianyun Sun →
“OmniAssistBench: Assistant-style Interaction Benchmark for Omni-LLMs”
- LinkedLinked via arxiv author · 85%Chaoyou Fu →
“OmniAssistBench: Assistant-style Interaction Benchmark for Omni-LLMs”
- LinkedLinked via arxiv author · 85%Zhengye Zhang →
“OmniAssistBench: Assistant-style Interaction Benchmark for Omni-LLMs”
- LinkedLinked via arxiv author · 85%Feiyang Duan →
“OmniAssistBench: Assistant-style Interaction Benchmark for Omni-LLMs”
- LinkedLinked via arxiv author · 85%Qingyuan Cao →
“OmniAssistBench: Assistant-style Interaction Benchmark for Omni-LLMs”
- LinkedLinked via arxiv author · 85%Yonghui Niu →
“OmniAssistBench: Assistant-style Interaction Benchmark for Omni-LLMs”