驾驭记忆：记忆智能体中记忆底座的全面评估

摘要

记忆正成为长周期大语言模型智能体的核心基础设施，然而现有评估对于在不同运行机制下应使用何种记忆基底——即记忆表征与存储所依托的底层介质——所提供的指导十分有限。我们针对记忆增强型智能体的记忆基底提出了一种受控的基准测试评估框架，涵盖密集与稀疏索引、文本记录、结构化存储、层次化存储、基于精炼的记忆、参数更新以及激活兼容的上下文机制。在三个基础模型和四个基准测试套件（涵盖以用户为中心的问题回答和以智能体为中心的决策制定）中，我们在统一框架下测度了26项性能与效率指标。结果表明，没有任何单一基底能够持续占据主导地位：广泛的检索有利于长上下文事实型问答，而过度的检索可能将注意力从决策关键上下文上转移，从而损害序贯决策。可扩展性进一步引入了一条路由轴，因为在中等历史长度下表现良好的基底在更长周期下可能变得代价高昂或脆弱不堪。这些发现表明，基底路由是自适应智能体记忆系统中不可或缺的组成部分，并为设计高效、可靠且具备机制感知能力的大语言模型智能体长期记忆提供了实证指导。代码将在论文被接收后公开。

English

Memory is becoming core infrastructure for long-horizon LLM agents, yet existing evaluations offer limited guidance on which memory substrate, namely the underlying medium in which memory is represented and stored, should be used under different operating regimes. We present a controlled harness evaluation of memory substrates for memory-augmented agents, covering dense and sparse indices, text records, structural stores, hierarchical stores, refinement-based memories, parametric updates, and activation-compatible context mechanisms. Across three backbone models and four benchmark suites spanning user-centric question answering and agent-centric decision-making, we instrument 26 performance and efficiency metrics under a unified harness. Our results show that no single substrate consistently dominates: broad retrieval benefits long-context factual QA, while excessive retrieval can harm sequential decision-making by shifting attention away from action-critical context. Scalability introduces a further routing axis, as substrates that perform well at moderate history lengths can become costly or brittle at longer horizons. These findings motivate substrate routing as a necessary component of adaptive agent memory systems and provide empirical guidance for designing efficient, reliable, and regime-aware long-term memory for LLM agents. Code will be made available upon acceptance.