Infinite-Parameter LLMs generate weights from live data

Shifting conversational adaptation from bloated KV-cache prompts into low-rank dynamic weight modulations is an elegant paradigm shift, though ensuring long-term Bayesian calibration without weight drift or catastrophic forgetting remains the real hurdle.

• –Bypasses context window saturation by writing interactive knowledge and user corrections directly into feed-forward weights.
• –Implements online Bayesian updates over latent representations, allowing effective weights to continuously evolve across interaction turns.
• –Captures the expressive scaling benefits of massive MoE banks without requiring an expanding physical storage footprint.
• –Amortizes compute over long multi-turn sessions compared to repeatedly reprocessing long context histories with standard in-context learning or RAG.