A new study from researchers at Stanford University and Nvidia proposes a way for AI models to keep learning after deployment — without increasing inference costs. For enterprise agents that have to digest long docs, tickets, and logs, this is a bid to get “long memory” without paying attention costs that grow with context length. The approach, called “End-to-End Test-Time Training” (TTT-E2E), reframes language modeling as a continual learning problem: Instead of memorizing facts during pre-training, models learn how to adapt in real time as they process new information. The result is a Transformer that can match long-context accuracy of full attention models while running at near-RNN efficiency — a potential breakthrough for enterprise workloads where context length is colliding with cost. The accuracy-efficiency trade-off For developers building AI systems for long-document tasks, the choice of model architecture often involves a painful trade-off between accuracy and efficiency. On one side are Transformers with full self-attention, currently the gold standard for accuracy. They are designed to scan through the keys and values of all previous tokens …