GraphRAG

Most agent-memory systems still do the lazy thing: store raw interaction history, retrieve a few chunks, and hope the base model compresses the mess at inference time. PlugMem starts from a much stronger assumption. The useful part of experience is sparse, structured, and should be compiled before retrieval. That is why this paper matters. PlugMem was submitted to arXiv on February 6, 2026, published on the Microsoft Research site on March 6, 2026, and the PDF metadata marks it as an ICML 2026 proceedings paper. As of April 5, 2026, the code and benchmark artifacts are public. The claim is ambitious but concrete: a single task-agnostic memory module, attached unchanged to very different agents, can beat both raw-memory baselines and several task-specific memory systems while using much less agent-side context. S1 S2 S3 S4 ...

Introduction: The Memory Gap in Modern AI Systems The central challenge facing the next generation of advanced AI agents is the “Context Retention Challenge.” While architectures like Retrieval-Augmented Generation (RAG) have given agents access to vast external knowledge bases, they are often architecturally insufficient for dynamic enterprise environments where data, user preferences, and operational context evolve continuously. Traditional RAG systems, often powered by vector databases, are fundamentally reliant on static data sources. This design treats each interaction as an isolated event, preventing the system from building long-term memory or modeling the complex, relational dependencies inherent in the real world. ...