RAG Retrieves, It Never Remembers. A vendor-neutral blueprint for applications that accumulate understanding.
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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:2404.07729v2 Announce Type: replace Abstract: Continual learning (CL) evaluates adaptability in learning solutions to retain knowledge. Our research addresses the challenge of catastrophic forg...
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.
The paper introduces a new approach called Homeostatic Continual Learning, designed to allow an AI agent to learn continuously in a changing environment without catastrophic forgetting. The method identifies outliers in environmental data when the agent’s output deviates, enabling the agent to incrementally refine its model and policy across increasingly diverse contexts. The authors also propose extending the method to build a world model that factorizes objects into features, abstracts them into comparable concept instances, and maps concepts to intents via features, while outlining necessary future work and broader AI connections.
K-Bench is a new benchmark designed to evaluate large language model (LLM) unlearning when the models are deployed as agents. Unlike previous benchmarks that only inspect the final answer, K-Bench examines all six channels of a ReAct agent—including chain-of-thought, tool calls, tool observations, and elicited summaries—to determine if a secret is leaked. The benchmark measures leakage for secrets placed in the model weights, prompt, or retrieval store, and finds that many existing unlearning methods fail to prevent leaks in deployed agents, especially when secrets reside in the prompt or retrieval store.