Robust Omni-Modal Reasoning for Arbitrary Modality Combinations

Establish robust omni-modal reasoning methods that integrate arbitrary combinations of text, images, audio, and video, enabling reliable cross-modal integration beyond unimodal or pairwise settings.

Background

Current multimodal systems predominantly handle fixed modality pairs (e.g., text–image or text–video), and unified omni models trained jointly across modalities face performance trade-offs. The scarcity of datasets that require rich cross-modal integration further complicates progress toward general omni reasoning.

Agent-Omni proposes inference-time coordination of specialized foundation models to improve cross-modal reasoning, yet the broader field recognizes that achieving robust reasoning that seamlessly integrates arbitrary modality combinations remains unresolved.