Executive Summary

• The Google DeepMind mechanistic interpretability team has made a strategic pivot over the past year, from ambitious reverse-engineering to a focus on pragmatic interpretability:
  • Trying to directly solve problems on the critical path to AGI going well^[1]
  • Carefully choosing problems according to our comparative advantage
  • Measuring progress with empirical feedback on proxy tasks
• We believe that, on the margin, more researchers who share our goals should take a pragmatic approach to interpretability, both in industry and academia, and we call on people to join us
  • Our proposed scope is broad and includes much non-mech interp work, but we see this as the natural approach for mech interp researchers to have impact
  • Specifically, we’ve found that the skills, tools and tastes of mech interp researchers transfer well to important and neglected problems outside “classic” mech interp
  • See our companion piece for more on which research areas and theories of change we think are promising
• Why pivot now? We think that times have changed.
  • Models are far more capable, bringing new questions within empirical reach
  • We have been disappointed by the amount of progress made by ambitious mech interp work, from both us and others^[2]
  • Most existing interpretability techniques struggle on today’s important behaviours, e.g. they involve large models, complex environments, agentic behaviour and long chains of thought
• Problem: It is easy to do research that doesn't make real progress.
  • Our approach: ground your work with a North Star - a meaningful stepping-stone goal towards AGI going well - and a proxy task - empirical feedback that stops you fooling yourself and that tracks progress toward the North Star.
  • “Proxy tasks” doesn't mean boring benchmarks. Examples include: interpret the hidden goal of a model organism; stop emergent misalignment without changing training data; predict what prompt changes will stop an undesired behavior.
• We see two main approaches to research projects: focused projects (proxy task driven), and exploratory projects (curiosity-driven, proxy task validated)
  • Curiosity-driven work can be very effective, but can also get caught in rabbit holes. We recommend starting in a robustly useful setting, time box your exploration^[3], and finding a proxy task as a validation step^[4]
• We advocate method minimalism: start solving your proxy task with the simplest methods (e.g. prompting, steering, probing, reading chain-of-thought). Introduce complexity or design new methods only once baselines have failed.

Read the full post here, and the companion piece on promising AGI Safety relevant research directions here