This episode introduces TTT-E2E, a novel method for long-context language modeling that treats context processing as a continual learning problem rather than a structural design challenge. Instead of relying on traditional attention mechanisms that slow down as text grows, the model compresses information into its internal weights by learning at test time through next-token prediction. By utilizing meta-learning during the initial training phase, the authors optimize the model's ability to update itself efficiently on new sequences. Experiments on 3B-parameter models demonstrate that this approach maintains the performance of full-attention Transformers while achieving 2.7× faster inference at 128K context lengths. Ultimately, the method offers a hardware-efficient alternative to RNNs and Transformers by providing constant inference latency without sacrificing the ability to leverage massive amounts of data