When Your Own Output Becomes Your Training Data: Noise-to-Meaning Loops and a Formal RSI Trigger
Rintaro Ando·May 05, 2025
Summary
The N2M-RSI model formalizes AI self-improvement, linking self-prompting and AutoML, suggesting super-linear effects in multi-agent systems. It ensures each step adds a positive increment, maintaining safety and learning. The model uses a noise-to-meaning operator and symmetrized KL divergence for averaging KL divergence in both directions. Contributions from researchers like Tom B. Brown, Mark Chen, and Yejin Jeon are highlighted in AI advancements from 2014-2025, focusing on superintelligence, language models, neural architecture, and conversational systems. Notable papers include studies on AI evaluation, architecture, and conversational systems.
Introduction
Background
Overview of AI self-improvement concepts
Objective
The aim of the N2M-RSI model in formalizing AI self-improvement
The N2M-RSI Model
Core Mechanisms
Noise-to-meaning operator
Symmetrized KL divergence for averaging KL divergence
Multi-Agent Systems
Explanation of super-linear effects in multi-agent systems
How the model ensures each step adds a positive increment for safety and learning
Linking Self-Prompting and AutoML
Self-Prompting
Definition and role in AI self-improvement
AutoML
Overview of AutoML and its integration with self-prompting
Synergy
How the N2M-RSI model combines self-prompting and AutoML for enhanced AI capabilities
Contributions from Key Researchers
Tom B. Brown
Contributions to AI advancements
Mark Chen
Research focus and contributions
Yejin Jeon
Work on superintelligence, language models, neural architecture, and conversational systems
Notable Papers and Research
AI Evaluation
Studies on evaluating AI systems
Architecture
Research on neural architecture and its impact on AI self-improvement
Conversational Systems
Contributions to the development of conversational AI systems
Future Directions and Implications
Challenges
Potential challenges in implementing the N2M-RSI model
Opportunities
Opportunities for further research and development
Ethical Considerations
Discussion on ethical implications of AI self-improvement
Basic info
papers
computation and language
machine learning
artificial intelligence
Advanced features
Insights
What role does the noise-to-meaning operator play in the N2M-RSI model's functionality?
How does the N2M-RSI model integrate self-prompting with AutoML to achieve AI self-improvement?
In what ways does the N2M-RSI model ensure safety and learning in multi-agent systems?
What are the super-linear effects observed in multi-agent systems as suggested by the N2M-RSI model?