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/

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