Do foundation models genuinely understand data?

Determine whether foundation models trained via self-supervision on broad datasets genuinely understand the data they are trained on, or whether they merely learn statistical patterns without true understanding.

Background

Within the discussion of foundation models, the paper highlights the rapid advances in generative and reasoning capabilities while questioning the nature of what these models actually capture during training. The authors emphasize that, despite impressive performance, trustworthiness depends on explainability and genuine understanding rather than pattern memorization.

This uncertainty directly impacts sensitive multimedia analytics applications, where expert users must rely on accurate and interpretable AI behavior. Clarifying whether foundation models exhibit true understanding is foundational to designing reliable human-AI teaming and to developing appropriate evaluation and trust-building mechanisms.