让大模型具备持续记忆与时间连贯性的新型记忆架构。
Continuum Memory Architectures for Long-Horizon LLM Agents
- 用持久存储和关联路由实现记忆的动态更新
- 在知识更新等任务中显著优于传统检索增强机制
- 适合需要长期规划与上下文理解的智能体应用
检索增强生成（RAG）已成为为大语言模型（LLM）代理提供上下文知识的标准策略。然而，RAG将记忆视为无状态的查表：信息永久存在，检索仅读不写，缺乏时间连续性。我们提出 extit{连续体记忆架构}（CMA），一类通过持久存储、选择性保留、关联路由、时间链式连接及高阶抽象整合来维持并更新内部状态的系统。我们不披露具体实现，而是定义CMA所要求的架构特性，并在暴露RAG结构性缺陷的任务中展示其一致的行为优势：知识更新、时间关联、联想回忆与上下文消歧。实证测试表明，CMA是长时程智能体的必要架构基础，同时揭示了延迟、漂移与可解释性方面的开放挑战。
原文摘要 · Abstract (English)
Retrieval-augmented generation (RAG) has become the default strategy for providing large language model (LLM) agents with contextual knowledge. Yet RAG treats memory as a stateless lookup table: information persists indefinitely, retrieval is read-only, and temporal continuity is absent. We define the \textit{Continuum Memory Architecture} (CMA), a class of systems that maintain and update internal state across interactions through persistent storage, selective retention, associative routing, temporal chaining, and consolidation into higher-order abstractions. Rather than disclosing implementation specifics, we specify the architectural requirements CMA imposes and show consistent behavioral advantages on tasks that expose RAG's structural inability to accumulate, mutate, or disambiguate memory. The empirical probes (knowledge updates, temporal association, associative recall, contextual disambiguation) demonstrate that CMA is a necessary architectural primitive for long-horizon agents while highlighting open challenges around latency, drift, and interpretability.
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