Part 4/9:
At its core, the AI Co-Scientist embodies a unique architecture that employs test time compute scaling to iteratively refine its outputs. This novel approach allows the AI to engage in self-critique and leverage various tools to enhance its research proposals. By implementing tournaments for hypothesis comparison, the system systematically ranks ideas, allowing the most promising ones to rise to the top. The use of ELO Auto-evaluation metrics derived from competitive ranking processes provides insights into the quality of hypotheses generated by the AI.