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A Pragmatic Vision for Interpretability — AI Alignment Forum

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The DeepMind mech interp team has pivoted from chasing the ambitious goal of complete reverse-engineering of neural networks, to a focus on pragmatically making as much progress as we can on the critical path to preparing for AGI to go well, and choosing the most important problems according to our comparative advantage. We believe that this pragmatic approach has already shown itself to be more promising. We don’t claim that these ideas are unique, indeed we’ve been helped to these conclusions by the thoughts of many others [[5]] . But we have found this framework helpful for accelerating our progress, and hope to distill and communicate it to help other have more impact. We close with recommendations for how interested researchers can proceed. Consider the recent work by Jack Lindsey's team at Anthropic on steering Sonnet 4.5 against evaluation awareness, to help with a pre-deployment audit. When Anthropic evaluated Sonnet 4.5 on their existing alignment tests [[6]] , they found that

x A Pragmatic Vision for Interpretability — AI Alignment Forum GDM Interp Progress Updates Interpretability (ML & AI) AI Frontpage 2025 Top Fifty: 44 % 60 A Pragmatic Vision for Interpretability by Neel Nanda , Josh Engels , Arthur Conmy , Senthooran Rajamanoharan , bilalchughtai , CallumMcDougall , János Kramár , lewis smith 1st Dec 2025 32 min read 39 60 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 crit

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