Title: MemVerse: Multimodal Memory for Lifelong Learning Agents

Abstract:

Although large-scale language and vision models have advanced rapidly, AI agents remain constrained by a critical deficiency: the inability to retain information. Lacking dependable memory systems, these agents are prone to catastrophic forgetting of prior experiences, encounter difficulties with extended reasoning tasks, and struggle to maintain coherent operations within interactive or multimodal settings. To address this, we present MemVerse, a flexible, model-agnostic memory framework designed to integrate hierarchical retrieval-based memory with fast parametric recall. This approach facilitates scalable and adaptive multimodal capabilities.

MemVerse manages short-term memory to preserve recent context, while simultaneously converting unstructured multimodal inputs into organized long-term memories structured as hierarchical knowledge graphs. This architecture enables continuous consolidation, selective forgetting, and controlled memory expansion. To meet real-time processing requirements, MemVerse employs a periodic distillation process that transfers key insights from long-term memory into the parametric model. This mechanism ensures rapid, differentiable access to information without sacrificing interpretability. Comprehensive evaluations reveal that MemVerse markedly enhances both continual learning efficiency and multimodal reasoning, allowing agents to maintain coherence, adapt, and recall effectively throughout prolonged interactions.

Source: arXiv Generated at: 2026-06-03 00:00:00 UTC