Dhaval Adjodah

Recursive Self-Improvement and the Future of AI

Martin Casado, Dhaval Adjodah, Owen He

This team presents Recursive Self-Improvement as a closed-loop system where the AI optimizes its own cognitive architecture alongside physical hardware and prompts, distinguishing their approach from human-guided autocatalysis. By employing a multi-agent framework with a dedicated "Viceroy of Variance" to stress-test results, the group reports early empirical evidence of simultaneous performance gains and cost reductions that defy traditional diminishing returns. While acknowledging physical limits like Planck's constant and Amdahl's Law, the speakers assert their first-principles mathematical foundation enables domain-specific acceleration, with verified updates expected imminently following rigorous internal replication.