Google HOPE Model: The Self-Learning AI That Rewrites Its Own Rules

Google’s HOPE Model Drops: A Self-Editing Neural Net That Keeps Learning After Training HOPE uses Nested Learning to update its own weights at inference time, beating Transformer, RetNet and Mamba on 10 benchmarks—with only 1.3 B parameters. Featured Snippet Q&A Q: What makes Google’s HOPE architecture different from Transformer? A: HOPE treats every layer as a nested optimizer that can modify its own weights during inference, enabling lifelong learning without catastrophic forgetting. Hook (3-second rule) Your LLM stops learning the moment you ship it. Google’s new HOPE model doesn’t. It keeps re-writing its own weights while users type—think of it …