MAGMA：面向AI智能体的多圖譜驅動能動記憶架構

摘要

記憶增強生成（MAG）技術通過為大型語言模型擴展外部記憶來支持長上下文推理，但現有方法主要依賴於對單體式記憶存儲進行語義相似度檢索，導致時序、因果和實體信息相互糾纏。這種設計限制了查詢意圖與檢索證據之間的可解釋性與對齊度，從而影響推理準確性。本文提出MAGMA——一種多圖譜智能記憶架構，將每個記憶項映射至正交的語義、時序、因果和實體圖譜中。MAGMA將檢索過程定義為基於策略指導的關係圖譜遍歷，實現查詢自適應的選擇與結構化上下文構建。通過解耦記憶表徵與檢索邏輯，該架構提供了透明的推理路徑和細粒度的檢索控制。在LoCoMo和LongMemEval數據集上的實驗表明，MAGMA在長程推理任務中持續優於當前最先進的智能記憶系統。

English

Memory-Augmented Generation (MAG) extends Large Language Models with external memory to support long-context reasoning, but existing approaches largely rely on semantic similarity over monolithic memory stores, entangling temporal, causal, and entity information. This design limits interpretability and alignment between query intent and retrieved evidence, leading to suboptimal reasoning accuracy. In this paper, we propose MAGMA, a multi-graph agentic memory architecture that represents each memory item across orthogonal semantic, temporal, causal, and entity graphs. MAGMA formulates retrieval as policy-guided traversal over these relational views, enabling query-adaptive selection and structured context construction. By decoupling memory representation from retrieval logic, MAGMA provides transparent reasoning paths and fine-grained control over retrieval. Experiments on LoCoMo and LongMemEval demonstrate that MAGMA consistently outperforms state-of-the-art agentic memory systems in long-horizon reasoning tasks.

MAGMA：面向AI智能体的多圖譜驅動能動記憶架構

MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents

摘要

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