RAG Retrieves, It Never Remembers. A vendor-neutral blueprint for applications that accumulate understanding.
Summary generated by The Flow from the publisher's feed. The full article lives at Towards Data Science.
arXiv:2604. 11364v2 Announce Type: replace Abstract: The two most influential cognitive architecture frameworks for AI agents, CoALA [21] and JEPA [12], both lack an explicit Knowledge layer with its own persistence semantics.
arXiv:2606. 26511v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) gives agents access to accumulated knowledge, but has no model of time.
Vector databases are a temporary bridge. Discover why the next AI infrastructure revolution relies on persistent neural state and strict latency budgets, not on vector databases.
arXiv:2601. 22601v3 Announce Type: replace Abstract: Federated unlearning (FU) aims to erase knowledge from a global model.
arXiv:2606. 05661v1 Announce Type: new Abstract: Continual learning, the ability of AI systems to improve through sequential experience, has attracted substantial interest, but no high-quality benchmark exists to evaluate it.
arXiv:2505. 12239v2 Announce Type: replace-cross Abstract: In Continual Learning (CL), using a Pre-Trained Model (PTM) as the feature extractor has become a popular practice.