When Models Learn

Explains test-time training through the analogy of a GPS learning a persistent shortcut around daily traffic rather than a one-time reroute: the model takes a gradient step on the prompt it's answering, so its weights change as it works. Traces three implications, flat memory instead of a linearly growing KV-cache, the provider cost of serving a separate model per user, & faster inference, then…