Test‑time learning as permanent memory

Models are moving from static weights plus ephemeral context to architectures that compress ongoing context into their weights at inference time (test‑time training). This approach promises constant‑latency long‑context comprehension and continuous personalization by integrating conversation history as training data rather than storing it verbatim. — If test‑time learning becomes standard, it will change privacy, compute economics, auditability, and who controls model evolution—requiring new governance (provenance, update logs, liability and verification) and altering the pace of capability diffusion.