Continual learning and the post-monolith AI era

Continual learning is inseparable from specialization because the cost of remembering scales with scope: as context grows, the KV cache grows linearly and can exceed all model weights, so information must move from token level into weights. Task specialization is essentially solved via continuous fine-tuning pipelines, while broad-scope, sparse-feedback, one-shot retention remains the hard problem.