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

Recent advances in large language models have driven the emergence of intelligent agents operating in open-world, multimodal environments. To support 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 supporting analytical, deductive reasoning critical for real-world decision making. To address this limitation, we propose NS-Mem, a long-term neuro-symbolic memory framework designed to advance multimodal agent reasoning by integrating neural memory with explicit symbolic structures and rules. Specifically, NS-Mem is organized around three core components of a memory system: (1) a three-layer memory architecture that consists of an episodic layer, a semantic layer, and a logic layer, (2) a memory construction and maintenance mechanism implemented by SK-Gen that automatically consolidates structured knowledge from accumulated multimodal experiences and incrementally updates both neural representations and symbolic rules, and (3) a hybrid memory retrieval mechanism that combines similarity-based search with deterministic symbolic query functions to support structured reasoning. Experiments on real-world multimodal reasoning benchmarks demonstrate that Neuro-Symbolic Memory achieves an average 4.35 percentage points improvement in overall reasoning accuracy over pure neural memory systems, with gains of up to 12.5 percentage points on constrained reasoning queries, validating the effectiveness of NS-Mem.