🧠 Mind2Web 2: Evaluating Agentic Search with Agent-as-a-Judge Agentic Search is one of the most promising applications of modern AI. Imagine a virtual assistant that doesn’t just look up information for you but can autonomously search the web, navigate pages, find facts, and return well-structured answers with citations. That’s the idea behind tools like OpenAI’s Deep Research. However, how do we evaluate if such an AI is doing a good job? ...

Can a machine discover the laws of physics by itself—like Newton, but without the apple and without writing the equation by hand? In June 2025, a new method called H-FEX (Hamiltonian Finite Expression) was published. It doesn’t just predict system behavior—it writes down the math behind it. And crucially, in a form humans can understand. It’s a form of symbolic learning, increasingly popular over black-box neural networks that work, but don’t tell us why. ...

Imagine having to choose the best ad variant, but each time you only learn how many users clicked on the one you showed. This is the essence of bandit learning: it balances exploration (trying out new options) with exploitation (using the current best) to discover the winner as quickly as possible. In a world where every experiment has a cost—from ad budgets to a patient’s time in experimental therapy—bandit algorithms can significantly accelerate optimal decision-making. Yet, despite their practical power, these solutions are surprisingly hard to analyze theoretically! ...