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

This research proposes an “Infinite-Parameter LLM” that generates and updates model weights from live interaction data, allowing an AI system to learn from user-provided facts and corrections during a session rather than relying only on static pretraining or repeated prompting. For CIOs and technology leaders, the business value is in potentially better personalization, stronger session-to-session continuity, and reduced context-window pressure, which could improve the performance of enterprise assistants, support tools, and workflow copilots. Strategically, it points to a shift in AI architecture from retrieval-heavy, prompt-centric systems toward models that adapt online, which will require IT organizations to rethink governance, observability, latency/cost tradeoffs, and controls for how runtime knowledge is stored and used.