What if AI systems could improve themselves?
The Recursive Self-Improvement (RSI) project explores AI architectures capable of continuously adapting, restructuring, and enhancing their own capabilities. Inspired by biological intelligence, we investigate mechanisms such as structural plasticity, adaptive learning, and open-ended evolution to move beyond the limitations of static neural networks.
Our goal is to develop self-evolving systems that can learn more efficiently, adapt to new challenges, and gradually increase their intelligence over time. We believe this direction represents a critical step toward the next generation of AI and, ultimately, Artificial General Intelligence (AGI).
enabling AI architectures to grow, reorganize, and adapt as complexity increases.
developing learning processes that can dynamically adjust beyond fixed optimization rules.
creating systems that continuously explore and acquire new capabilities rather than optimizing for a single predefined objective.
