EnSIMem：面向长期智能体记忆的实体结构化索引
EnSIMem: Entity-Structured Indexing for Long-Term Agent Memory
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中文总结 AI 辅助
EnSIMem通过实体结构化索引和情节级来源，为长期智能体记忆提供可靠基础，实现高准确率与高效在线推理。
中文摘要 AI 辅助
一个与用户长期互动的智能体必须从持续增长的交互历史中回忆事实、偏好、事件和变化。现有的记忆系统通常将交互压缩为通用摘要或检索匿名的文本块，这使得智能体难以识别正确的实体、属性和支持证据。我们提出了EnSIMem，一种面向智能体的实体结构化长期记忆架构。在离线构建阶段，系统将交互组织成主题连贯的情节，并构建对话基础的索引条目，形式为[实体][实体类型][属性:值]。每个条目保留其来源轮次、时间信息和可用的多模态字段。在线交互期间，智能体的请求被分解为证据需求，其属性与记忆索引对齐。实体-属性查找和自适应检索随后收集点、时间、组合和聚合推理所需的证据。智能体从保留的来源证据而非有损的记忆摘要中生成响应。在长期智能体记忆基准上，EnSIMem在保持紧凑上下文和良好在线效率的同时实现了高答案准确率。这些结果表明，实体结构化索引和情节级来源为智能体的长期记忆提供了可靠的基础。我们模型的代码可在https://github.com/RamonMeng/EnSIMem获取。
英文摘要
An agent that interacts with users over long periods must recall facts, preferences, events, and changes from a continuously growing interaction history. Existing memory systems often compress interactions into generic summaries or retrieve anonymous text chunks, making it difficult for an agent to identify the correct entity, property, and supporting evidence. We present EnSIMem, an entity-structured long-term memory architecture for an agent. During offline construction, the system organizes interactions into theme-coherent episodes and builds dialogue-grounded index entries of the form [entity][entity_type][property: value]. Each entry preserves its source turns, temporal information, and available multimodal fields. During online interaction, the agent's request is decomposed into evidence requirements whose properties are aligned with the memory index. Entity-property lookup and adaptive retrieval then collect the evidence needed for point, temporal, compositional, and aggregation reasoning. The agent generates its response from the preserved source evidence rather than from lossy memory summaries. On long-term agent-memory benchmarks, EnSIMem achieves high answer accuracy while maintaining compact, evidence-focused contexts. These results show that entity-structured indexing and episode-level provenance provide a reliable foundation for long-term memory in agents. The code of our model is available at https://github.com/RamonMeng/EnSIMem.
发表机构
- University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
- Amazon（亚马逊）
机构由 AI 辅助整理，请以论文原文为准。
