Advancing Multimodal Agent Reasoning with Long-Term Neuro-Symbolic Memory

ArXi:2603.15280v1 Announce Type: new Recent advances in large language models have driven the emergence of intelligent agents operating in open-world, multimodal environments. To long-term reasoning, such agents are typically equipped with external memory systems. However, most existing multimodal agent memories rely primarily on neural representations and vector-based retrieval, which are well-suited for inductive, intuitive reasoning but fundamentally limited in ing analytical, deductive reasoning critical for real-world decision making.