// Memory as a Model // The paper augments any LLM with a separate trained memory model that stores, retrieves, and integrates facts on its …

// Memory as a Model // The paper augments any LLM with a separate trained memory model that stores, retrieves, and integrates facts on its behalf. It decouples memory updates from base-model weight updates. It achieves continual-learning robustness without catastrophic forgetting, which is a property that RAG fails to deliver. A vector store is a database with a learned encoder bolted on. MeMo is a learned subsystem with explicit interfaces. That distinction matters, as agents need to be able to ingest fresh knowledge weekly without retraining or vector-DB churn. At its core, the position here is that memory in agents should be modular, learned, and gated, not a context-window hack. Paper: https://arxiv.org/abs/2605.15156 Learn to build effective AI agents in our academy: https://academy.dair.ai/

Source: DAIR.AI (X) | 2026-05-20