MemVerse：面向终身学习智能体的多模态记忆
MemVerse: Multimodal Memory for Lifelong Learning Agents
AI总结：
提出MemVerse，一种模型无关的即插即用记忆框架，通过分层检索记忆与参数化快速回忆结合，解决智能体在多模态交互中的灾难性遗忘和长程推理问题。
摘要：
尽管大规模语言和视觉模型取得了快速进展，但AI智能体仍然存在一个根本性限制：它们无法记忆。没有可靠的记忆，智能体会灾难性地遗忘过去的经验，难以进行长程推理，并且在多模态或交互环境中无法连贯地运行。我们提出了MemVerse，一种模型无关的即插即用记忆框架，它将快速的参数化回忆与基于检索的分层记忆相结合，实现了可扩展和自适应的多模态智能。MemVerse维护短期记忆以处理近期上下文，同时将原始多模态经验转化为结构化的长期记忆，组织为分层知识图谱。这种设计支持持续整合、自适应遗忘和有界的记忆增长。为了满足实时需求，MemVerse引入了一种周期性蒸馏机制，将长期记忆中的关键知识压缩到参数化模型中，从而实现快速、可微的回忆，同时保持可解释性。大量实验表明，MemVerse显著提高了多模态推理和持续学习效率，使智能体能够在扩展的交互中记忆、适应和连贯推理。
英文摘要：
Despite rapid progress in large-scale language and vision models, AI agents still suffer from a fundamental limitation: they cannot remember. Without reliable memory, agents catastrophically forget past experiences, struggle with long-horizon reasoning, and fail to operate coherently in multimodal or interactive environments. We introduce MemVerse, a model-agnostic, plug-and-play memory framework that bridges fast parametric recall with hierarchical retrieval-based memory, enabling scalable and adaptive multimodal intelligence. MemVerse maintains short-term memory for recent context while transforming raw multimodal experiences into structured long-term memories organized as hierarchical knowledge graphs. This design supports continual consolidation, adaptive forgetting, and bounded memory growth. To handle real-time demands, MemVerse introduces a periodic distillation mechanism that compresses essential knowledge from long-term memory into the parametric model, allowing fast, differentiable recall while preserving interpretability. Extensive experiments demonstrate that MemVerse significantly improves multimodal reasoning and continual learning efficiency, empowering agents to remember, adapt, and reason coherently across extended interactions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
