Memoria: 一种可扩展的代理记忆框架用于个性化对话式AI
Memoria: A Scalable Agentic Memory Framework for Personalized Conversational AI
AI总结：
Memoria通过结合动态摘要和加权知识图，为LLM提供持久记忆，实现对话连贯性和个性化，适用于需要适应性用户体验的行业应用。
摘要：
代理记忆正逐渐成为大型语言模型（LLM）维持连续性、个性化和长期上下文的关键使能器，在扩展用户交互中至关重要，是部署LLM作为真正交互和适应性代理的关键能力。代理记忆指的是为LLM提供类似代理的持久性记忆：在对话中保留和处理信息的能力，类似于人类的行为。我们提出了Memoria，一种模块化的记忆框架，通过持久、可解释和上下文丰富的记忆增强基于LLM的对话系统。Memoria集成了两个互补的组件：动态会话级摘要和基于加权知识图（KG）的用户建模引擎，该引擎逐步捕捉用户特征、偏好和行为模式作为结构实体和关系。这种混合架构能够在现代LLM的token约束下实现短期对话连贯性和长期个性化。我们展示了Memoria如何通过弥合无状态LLM接口和代理记忆系统之间的差距，实现可扩展、个性化的对话式人工智能（AI），为需要适应性和演进用户体验的行业应用提供实用解决方案。
英文摘要：
Agentic memory is emerging as a key enabler for large language models (LLM) to maintain continuity, personalization, and long-term context in extended user interactions, critical capabilities for deploying LLMs as truly interactive and adaptive agents. Agentic memory refers to the memory that provides an LLM with agent-like persistence: the ability to retain and act upon information across conversations, similar to how a human would. We present Memoria, a modular memory framework that augments LLM-based conversational systems with persistent, interpretable, and context-rich memory. Memoria integrates two complementary components: dynamic session-level summarization and a weighted knowledge graph (KG)-based user modelling engine that incrementally captures user traits, preferences, and behavioral patterns as structured entities and relationships. This hybrid architecture enables both short-term dialogue coherence and long-term personalization while operating within the token constraints of modern LLMs. We demonstrate how Memoria enables scalable, personalized conversational artificial intelligence (AI) by bridging the gap between stateless LLM interfaces and agentic memory systems, offering a practical solution for industry applications requiring adaptive and evolving user experiences.
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。
