Infinite-Parameter Large Language Models: Dynamic Weight Generation and Adaptation from Real-Time Data

This paper introduces Infinite-Parameter LLMs that dynamically generate and adapt model weights using real-time data through a Bayesian hypernetwork framework. The approach enables continuous learning from live interactions without fixed parameter banks, offering persistent knowledge retention and improved generalization over traditional methods.

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