According to Stanford AI Lab (@StanfordAILab), the newly released TTT-E2E framework enables large language models (LLMs) to continue training during deployment by using real-world context as training data to update their weights, resulting in significant improvements in memory and adaptability (source: https://x.com/karansdalal/status/2010774529120092481). Developed in collaboration with NVIDIA AI and Astera Institute, TTT-E2E addresses the industry's long-standing challenge of scalable LLM memory without requiring radical architecture changes (source: http://arxiv.org/abs/2512.23675). This approach allows AI models to learn from massive experience at the point of use, unlocking new business opportunities in adaptive AI solutions, enterprise automation, and customer personalization. By leveraging test-time training, companies can deploy LLMs that continuously improve, leading to enhanced product performance and reduced retraining costs (source: nvda.ws/4syfyMN).