Guardian Angels: LLM Personalization for Productivity and Security
The article proposes the development of 'Guardian Angels,' which are highly personalized LLMs designed to emulate a specific user's values and preferences. These digital twins aim to enhance individual productivity and provide a layer of cybersecurity against malicious AI-driven threats.
Why it matters
This concept addresses the principal-agent problem in AI, suggesting a path for individuals to maintain agency and security in an era of increasingly autonomous digital systems.
GPT , mind , personality , imitation learning , Decision Transformer , AI mode collapse , AI safety , transhumanism
I propose an approach for highly personalized LLMs, for near-future productivity gains and personal info/cybersecurity against increasingly powerful LLMs: they should, in the spirit of uploading, try to emulate the user’s values and preferences in order to amplify the principal—not replace them. I discuss a package of techniques and proposals to accomplish such ‘guardian angels’; dynamic evaluation of LLMs combined with active learning and elicitation and heavy inner-monologue search/data-augmentation.
Powerful LLMs will be deployed at global scale in the next few years, and will dominate the Internet, and increasingly, ordinary life. As of mid-2026, there is no coherent vision for how knowledge professionals, or ordinary people, will be able to harness these LLMs for large productivity increases, or how they will handle cybersecurity and cognitive security.
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