[论文解读] MemAdapter: Fast Alignment across Agent Memory Paradigms via Generative Subgraph Retrieval

Xin Zhang, Kailai Yang+5 more|arXiv (Cornell University)|Feb 9, 2026

Multimodal Machine Learning Applications被引用 0

一句话总结

MemAdapter 将异质代理记忆范式统一为一个生成式子图检索器，实现快速跨范式对齐并在基准上达到有竞争力的性能，且训练计算量极低。

ABSTRACT

Memory mechanism is a core component of LLM-based agents, enabling reasoning and knowledge discovery over long-horizon contexts. Existing agent memory systems are typically designed within isolated paradigms (e.g., explicit, parametric, or latent memory) with tightly coupled retrieval methods that hinder cross-paradigm generalization and fusion. In this work, we take a first step toward unifying heterogeneous memory paradigms within a single memory system. We propose MemAdapter, a memory retrieval framework that enables fast alignment across agent memory paradigms. MemAdapter adopts a two-stage training strategy: (1) training a generative subgraph retriever from the unified memory space, and (2) adapting the retriever to unseen memory paradigms by training a lightweight alignment module through contrastive learning. This design improves the flexibility for memory retrieval and substantially reduces alignment cost across paradigms. Comprehensive experiments on three public evaluation benchmarks demonstrate that the generative subgraph retriever consistently outperforms five strong agent memory systems across three memory paradigms and agent model scales. Notably, MemAdapter completes cross-paradigm alignment within 13 minutes on a single GPU, achieving superior performance over original memory retrievers with less than 5% of training compute. Furthermore, MemAdapter enables effective zero-shot fusion across memory paradigms, highlighting its potential as a plug-and-play solution for agent memory systems.

研究动机与目标

• 解决跨代理系统中记忆范式（显式、参数化、潜在）的碎片化问题。
• 提出 MemAdapter，采用两阶段训练策略以统一记忆空间并适应未见范式。
• 在基准测试中展示效率与有效性，包括跨范式零-shot 融合。
• 展示跨范式对齐可在较低的训练计算和时间成本下实现。

提出的方法

• 两阶段训练：(1) 从统一的记忆空间训练生成式子图检索器；(2) 通过对比学习训练的轻量级对齐模块使检索器适应未见记忆范式。
• 使用子图的生成检索实现灵活的跨范式对齐。
• 证明跨范式对齐在单 GPU 上13分钟内完成，且训练计算量小于总量的5%。
• 在五个强代理记忆系统、三种记忆范式和不同代理模型规模上对 MemAdapter 进行评估。

实验结果

研究问题

• RQ1 如何将异质的记忆范式统一为一个灵活的记忆系统？
• RQ2 生成式子图检索器是否能够实现快速且有效的跨范式对齐？
• RQ3 MemAdapter 在不同记忆范式和模型规模下的表现如何？
• RQ4 MemAdapter 在记忆范式之间是否可实现零-shot 融合？

主要发现

• 生成式子图检索器在三种范式和模型规模下始终超越五个强代理记忆系统的表现。
• 跨范式对齐在单 GPU 上13分钟内完成。
• MemAdapter 的训练计算量少于总量的5%，实现了卓越的性能。
• 该方法实现了跨记忆范式的有效零-shot 融合。