Infinite-Parameter LLMs: Generating and Adapting Weights from Live Data

The paper proposes an Infinite-Parameter LLM architecture that generates and adapts its weights from live interaction data using a compact hypernetwork and Bayesian updating, allowing the model to learn from real-time user input rather than relying solely on static pretraining. This approach keeps the stored footprint fixed while effectively enabling an infinite set of weights, potentially improving compute efficiency, freeing context windows, and providing persistent, generalizable knowledge across turns. The authors also outline an evaluation protocol to compare this method against traditional in-context learning and retrieval techniques.

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