Researchers propose a new approach to artificial intelligence research, called agentic auto-research. This method encourages AI systems to explore and search for solutions rather than simply optimizing for a final score. The authors argue that current methods can lead to overfitting and blind sampling, which hinders scientific discovery. They suggest using dense signals of epistemic progress to guide the next intervention, allowing the agent to make more efficient use of resources. This approach is demonstrated in a simulated physics environment, where an AI research agent discovers a hidden physical law. --- Why it matters: This matters because it challenges the conventional wisdom on how AI should be used for scientific discovery. By encouraging exploration and search, agentic auto-research could lead to more efficient and effective use of resources, potentially accelerating breakthroughs in various fields. Source: https://arxiv.org/abs/2608.09855