What Matters Right Now In Mechanistic Interpretability?
This is a talk I gave to my MATS 9.0 training scholars about the big picture of mech interp - as of Oct 2025, what had changed? What are the most important things to work on? What are common mistakes? How do I recommend approaching the field? Notes: https://docs.google.com/document/d/1d... 00:00:00 What Matters Now in Mech Interp 00:02:16 The Rise of Reasoning Models 00:04:49 Q&A: Architecture Changes 00:08:58 Research Priorities 00:10:13 AI Psychology & Misalignment 00:12:05 Debugging Model Failures 00:14:53 Understanding vs. Control 00:24:07 Case Study: Golden Gate Claude 00:30:00 A Pragmatic Research Philosophy 00:35:03 Pragmatism, Baselines, and Simplicity 00:46:12 Diagnosis vs. Solution 00:53:55 Do We Need Precision?

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How Reasoning Models Break Mechanistic Interpretability Techniques
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How AI Learned to Teach Itself [JEPA]

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The French Do Not Care About Work
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The Dark Matter of AI [Mechanistic Interpretability]

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Scaling interpretability

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Science of Misalignment

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Margin Call - "Sell it all. Today." 👆🤘👆

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Why you should care about AI interpretability - Mark Bissell, Goodfire AI
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Yann LeCun's $1B Bet Against LLMs [Part 1]

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If Prime Numbers Become Increasingly Rare, Then Why Do They Keep Showing Up In Pairs?

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