A new project introduces an autonomous reinforcement learning (RL) agent designed to achieve state-of-the-art (SOTA) performance in AI by reading academic papers and updating its cognition. The agent emphasizes a no-fluff design and ensures full on-chain transparency through glass-box logging of decisions and memory pruning. This approach is paired with a treasury mechanism, forming a tech-driven native crypto intelligent agent concept.