MOSS构建可审计的智能体记忆系统，让长期记忆透明可控。
Memory-Orchestrated Semantic System (MOSS): An Auditable Agentic Memory Architecture
- 用结构化数据库驱动检索，查询过程符号化且可复现。
- 一年持续运行，处理超4400万词元、569个概念与五百万关系。
- 适合需要长期知识积累与审计的团队或机构使用。
长期记忆仍是智能体的结构性短板。主流的检索增强生成（RAG）依赖基于嵌入的相似性搜索，天生不透明、难审计，且受限于向量表示的理论边界。我们提出记忆编排语义系统（MOSS），一种由智能体主导检索的结构化记忆架构。MOSS具备模型无关、存储无关、API无关特性：可在任意关系型引擎上运行，对接任意LLM服务商（或确定性非LLM流程），部署于本地或云环境。其检索执行为符号化且可复现（一旦查询制定，后续无LLM参与），系统从索引到答案生成每一步均被记录可查，实现内置可审计性。MOSS不强制外部本体，而是从语料自身推导概念词汇。报告了智能体记忆领域独一无二的纵向部署：一位学者工作语料连续运行一年（回溯至2024年10月，共4400万词元，事后索引），包含110,183个片段、163,494份文档、569个归纳出的概念、322,662次概念标注，以及总计约五百万关系的十一张元数据图谱，历经四代基础设施迭代。尽管当前案例为个人研究者，该架构绝不限于单人：适用于团队、机构等随时间累积知识的任何实体。我们认为，可审计、自主掌控、结构无边界的记忆，是智能体陪伴个人或组织多年而非仅会话级别的前提。
原文摘要 · Abstract (English)
Long-term memory remains a structural weakness of AI agents. The dominant approach, retrieval-augmented generation (RAG), relies on embedding-based similarity search, which is opaque by construction, difficult to audit, and bounded by the theoretical limits of vector representations. We present the Memory-Orchestrated Semantic System (MOSS), an agentic memory architecture in which the agent drives retrieval over a structured relational database. MOSS is model-agnostic, storage-agnostic, and API-agnostic: it runs on any relational engine, connects to any LLM provider (or to deterministic non-LLM processes), and deploys on any infrastructure, local or cloud. Its retrieval execution is symbolic and reproducible (once a query is formulated, no LLM participates in the retrieval loop) and every step of the system, from indexing to answer formulation, is logged and inspectable, making MOSS auditable by construction. Rather than imposing an external ontology, MOSS derives its conceptual vocabulary from the corpus itself. We report on a longitudinal deployment unique in the agentic-memory literature: a year of continuous production over an individual scholar's working corpus--a conversational corpus reaching back to October 2024 (some 44 million tokens, retroactively indexed) comprising 110,183 segments, alongside 163,494 catalogued documents, 569 inductively derived concepts, 322,662 concept annotations, and eleven metadata graphs totaling approximately five million relations--across four successive infrastructure generations. While the present case is that of a single researcher, the architecture is in no way specific to one person: it serves a team, an institution, or any entity that accumulates knowledge over time. We argue that auditable, sovereign, structurally unbounded memory is a precondition for AI agents intended to accompany a person or an organization over years rather than sessions.
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