Agent memory systems must accommodate continuously growing information while supporting efficient, context-aware retrieval for downstream tasks. Abstraction is essential for scaling agent memory, yet it often comes at the cost of specificity, obscuring the fine-grained details required for effective reasoning. We introduce Memora, a harmonic memory representation that structurally balances abstraction and specificity. Memora organizes information via its primary abstractions that index concrete memory values and consolidate related updates into unified memory entries, while cue anchors expand retrieval access across diverse aspects of the memory and connect related memories. Building on this structure, we employ a retrieval policy that actively exploits these memory connections to retrieve relevant information beyond direct semantic similarity. Theoretically, we show that standard Retrieval-Augmented Generation (RAG) and Knowledge Graph (KG)-based memory systems emerge as special cases of our framework. Empirically, Memora establishes a new state-of-the-art on the LoCoMo and LongMemEval benchmarks, demonstrating better retrieval relevance and reasoning effectiveness as memory scales.

翻译：智能体记忆系统必须适应持续增长的信息，同时支持面向下游任务的高效、上下文感知检索。抽象对于扩展智能体记忆至关重要，但它往往以牺牲具体性为代价，从而模糊了有效推理所需的细粒度细节。我们提出了Memora，一种在结构上平衡抽象性与具体性的和谐记忆表示。Memora通过其主抽象来组织信息，这些主抽象索引具体的记忆值并将相关更新整合为统一的记忆条目，而线索锚点则扩展了跨记忆不同方面的检索访问并连接相关记忆。基于此结构，我们采用一种检索策略，主动利用这些记忆连接来检索超出直接语义相似性的相关信息。理论上，我们证明了标准的检索增强生成（RAG）和基于知识图谱（KG）的记忆系统是我们框架的特例。实证上，Memora在LoCoMo和LongMemEval基准测试中确立了新的最先进水平，展示了在记忆扩展时更好的检索相关性和推理有效性。