Models that update their weights from ongoing experience the way humans do, instead of being frozen after pre-training and refined only via fine-tuning or RLHF.
Tensions
Continual learning collides with current safety, eval, and deployment assumptions: a model whose weights drift in production cannot be re-certified against a fixed benchmark. The unlock is also the failure mode — sustained drift without guardrails is how alignment problems become operational problems.