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-offFor 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 for every new token generated, providing them with lossless recall. However, this precision comes at a steep cost: The computational cost per token grows significantly with context length. On the other side are linear-time sequence models, which keep inference costs constant but struggle to retain information over very long contexts. Other approaches try to split the difference — sliding-window attention, hybrids that mix attention with recurrence, and other efficiency tricks — but they still tend to fall short of full attention on hard language modeling.The researchers’ bet is that the missing ingredient is compression: Inst …
