Vincent Koc argues that static benchmarks are failing in the era of adaptive AI. He proposes a shift from static testing to 'malleable evals,' where agents self-optimize and curate their own test suites based on user intent and production data, treating evaluation as a living, evolving system.
Daniel Lütgehetmann of inait introduces "digital brains," biologically accurate computational models of real brains, as a solution to current AI's limitations in physical world interaction. Unlike traditional AI that struggles with dynamic environments and skill accumulation, these digital brains leverage biologically inspired learning rules to achieve dramatically faster learning in robotics and complex systems, demonstrating potential for real-world adaptability and efficiency.