Beyond Understanding: Learning Neuro-Symbolic Representations for Agentic Intelligence

Multimodal foundation models learn powerful representations across heterogeneous modalities, but representations optimized for semantic understanding may be insufficient for agentic intelligence, where agents must reason over evolving environments, predict future states, and make goal-directed decisions. We introduce Neuro-Symbolic Multimodal Representation Learning for Agentic Intelligence, a paradigm that jointly learns neural and symbolic representations capturing semantic, relational, temporal, causal, and action-relevant structure. Our framework forms an evolving internal world representation in which symbolic structures guide neural learning, prediction, reasoning, and action, while new observations continuously refine the symbolic state. We propose an evaluation framework that shifts multimodal representation learning from semantic alignment toward prediction and decision utility, measuring compositional generalization, robustness to multimodal uncertainty, temporal prediction, and downstream decision-making. Our work establishes a foundation for multimodal representations designed not only for understanding the world, but for acting within it.