Balancing Rigor and Utility: A Review of "A Pragmatic Vision for Interpretability"

By Sohybe Ibrahim Abdelwahab Amer | June 2026The Google DeepMind mechanistic interpretability team (Neel Nanda et al.) suggested a deliberate shift; instead of relying on reverse-engineering of model internals, they proposed validating interpretability tools against proxy tasks that keep tracking safety towards a "North Star". I think this is broadly the right call backed up by the team's results; subtracting an "eval-awareness" vector from Claude Sonnet 4.5's activations turned a suspicious 0% ...