Summary
Foundation models are large artificial intelligence (AI) systems that learn from vast data and now play a central role in science and technology. Yet the complexity of how these models process information makes it difficult to recognize when their outputs are unreliable. This is especially challenging in safety-critical settings, such as health monitoring and smart homes. As hardware technologies evolve, new forms of data emerge from new sensors, and often do not align with past data used to train existing models. How these models encode information might also be disconnected from established
