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A new class of large language models that iteratively improve their own training by self-optimization. This research introduces the most efficient LLM architecture to date that requires less training data but achieves high accuracy and robustness.
Researchers combine the principles of quantum computing and machine learning to create a novel quantum-based neural network optimizer with unprecedented scalability and speed. Suitable for both Nuclear fusion and drug development simulations.