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 →
- alphaXiv
- arXiv
- CatalyzeX
- DagsHub
- Gemini 3-Pro
- Gotit.pub
- Hugging Face
- OmniAssistBench
- Omni-LLMs
- Qwen3-Omni-Instruct
- ScienceCast
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