arXiv:2603.16859v2 Announce Type: replace Abstract: Omni-modal large language models (OLMs) redefine human-machine interaction by natively integrating audio, vision, and text. However, existing OLM benchmarks remain anchored to static, accuracy-centric tasks, leaving a critical gap in assessing social interactivity, the fundamental capacity to navigate dynamic cues in natural dialogues. To this end, we propose SocialOmni, a comprehensive benchmark that operationalizes the evaluation of this conversational interactivity across three core dimensions: (i) speaker separation and identification (wh
The rapid advancement of omni-modal large language models necessitates new evaluation benchmarks to address complex, dynamic interactions beyond static tasks.
Measuring social interactivity in large language models is crucial for their effective integration into human environments and for developing truly 'intelligent' agents.
The focus of OLM evaluation expands from mere accuracy to assessing nuanced social interactivity, driving development towards more context-aware and conversational AI.
- · AI developers focused on social intelligence
- · Companies building conversational AI products
- · Research institutions specializing in human-AI interaction
- · OLM developers relying solely on static benchmarks
- · Companies with AI models lacking social processing capabilities
The benchmark will guide the development of OLMs towards more natural and socially capable interaction.
Improved social interactivity in OLMs could lead to wider adoption in customer service, education, and personal assistance.
As AI becomes more socially adept, ethical concerns around manipulation and the nature of human-AI relationships will intensify.
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Read at arXiv cs.AI