What if AI systems could learn continuously without repeatedly retraining the model underneath them? At HDC Labs, we are exploring hyperdimensional computing as a representation layer for online, associative learning. Neural models provide powerful learned representations and general capabilities; hyperdimensional representations provide a lightweight structure in which new observations, relationships, corrections, and outcomes can be incorporated as they arrive. Because learning can happen through simple composable operations over these representations, useful behavior can emerge from relatively few examples rather than large retraining datasets.

That same compositional structure also creates an opportunity for greater explainability. Instead of treating every update as an opaque change to model weights, hyperdimensional representations can preserve the primitives and associations that contributed to a result, making retrieval and reasoning easier to inspect. In this talk, we will show how HDC can complement modern ML systems with three capabilities that matter increasingly in the agentic age: explainable representations, continuous online learning, and sample-efficient adaptation.