Is Extending Modality The Right Path Towards Omni-Modality?

Tinghui Zhu, Kai Zhang, Muhao Chen, Yu Su

Is Extending Modality The Right Path Towards Omni-Modality?: 24 upvotes on Hugging Face Daily Papers, #8 of 24 papers on 2025-06-09. Day-by-day upvote history.

Omni-modal language models (OLMs) aim to integrate and reason over diverse input modalities--such as text, images, video, and audio--while maintaining strong language capabilities. Despite recent advancements, existing models, especially open-source ones, remain far from true omni-modality, struggling to generalize beyond the specific modality pairs they are trained on or to achieve strong performance when processing multi-modal inputs. We study the effect of extending modality, the dominant technique for training multimodal models, where an off-the-shelf language model is fine-tuned on target-domain and language data. Specifically, we investigate three key questions: (1) Does modality extension compromise core language abilities? (2) Can model merging effectively integrate independently fine-tuned modality-specific models to achieve omni-modality? (3) Does omni-modality extension lead to better knowledge sharing and generalization compared to sequential extension? Through extensive experiments, we analyze these trade-offs and provide insights into the feasibility of achieving true omni-modality using current approaches.

Paper page on Hugging Face · arXiv

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