This December 31, 2025 NVIDIA research introduces TTT-E2E, a novel approach to large language model memory that treats long-context processing as a continual learning problem rather than a structural design challenge. By utilizing test-time training, the model effectively compresses context into its own weights through next-token prediction, allowing it to adapt and learn while processing new information. Unlike traditional Transformers that suffer from linear latency growth, or Recurrent Neural Networks that experience performance loss at scale, TTT-E2E maintains constant inference speed without sacrificing accuracy. The method employs meta-learning during the pre-training phase to optimize the model’s initialization for these rapid weight updates at test time. Experimental results demonstrate that TTT-E2E achieves a 35x speedup over full attention at extreme context lengths while matching its scaling efficiency. Ultimately, the authors propose this end-to-end formulation as a fundamental solution to the computational bottlenecks of processing massive datasets.Sources:https://arxiv.org/pdf/2512.23675https://developer.nvidia.com/blog/reimagining-llm-memory-using-context-as-training-data-unlocks-models-that-learn-at-test-time/