Drive evaluation-to-model-improvement loops with training, post-training, data curation, simulation, robotics, SDG, WAM, and applied research teams, including failure discovery, data generation, post-training priorities, model roadmap feedback, and re-evaluation. Develop open-system and mechanistic evaluation methods using model internals, including representation probing, causal interventions, activation analysis, ablations, sparse autoencoders, attention and feature analysis, and circuit-style diagnostics.