Cross-Modal Coreference Alignment: Enabling Reliable Information Transfer in Omni-LLMs
Omni Large Language Models (Omni-LLMs) have demonstrated impressive capabilities in holistic multi-modal perception, yet they consistently falter in complex scenarios requiring synergistic omni-modal reasoning. Beyond understanding global multimodal context, effective reasoning also hinges on fine-grained cross-modal alignment, especially identifying shared referents across modalities, yet this aspect has been largely overlooked. To bridge this gap, we formalize the challenge as a cross-modal coreference problem, where a model must localize a referent in a source modality and re-identify it in a target modality. Building on this paradigm, we introduce CrossOmni, a dataset comprising nine ...
| Authors | Hongcheng Liu, Yuhao Wang, Zhe Chen et al. |
|---|---|
| Primary category | cs.CL |
| Published | 7 April 2026 |
| Last updated at source | 7 April 2026 |
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