[2501.16496] Open Problems in Mechanistic Interpretability - arXiv

††footnotetext: ∗Correspondence to: lee@apolloresearch.ai and brchughtaii@gmail.com††footnotetext: †Work done prior to joining Anthropic.

# Open Problems in Mechanistic Interpretability

Lee Sharkey∗Apollo Research
Bilal Chughtai∗Apollo Research

Joshua Batson Anthropic
Jack Lindsey Anthropic
Jeff Wu Anthropic†Lucius Bushnaq Apollo Research
Nicholas Goldowsky-Dill Apollo Research
Stefan Heimersheim Apollo Research
Alejandro Ortega Apollo Research
Joseph Bloom Decode Research
Stella Biderman Eleuther AI
Adria Garriga-Alonso FAR AI
Arthur Conmy Google DeepMind
Neel Nanda Google DeepMind
Jessica Rumbelow Leap Laboratories
Martin Wattenberg Harvard University
Nandi Schoots King’s College London and Imperial College London
Joseph Miller MATS
Eric J. Michaud MIT
Stephen Casper MIT
Max Tegmark MIT
William Saunders METR
David Bau Northeastern University
Eric Todd Northeastern University
Atticus Geiger Pr(AI)2r group
Mor Geva Tel Aviv University
Jesse Hoogland Timaeus
Daniel Murfet University of Melbourne

Tom McGrath Goodfire

###### Abstract

Mechanistic interpretability aims to understand the computational mechanisms underlying neural networks’ capabilities in order to accomplish concrete scientific and engineering goals. Progress in this field thus promises to provide greater assurance over AI system behavior and shed light on exciting scientific questions about the nature of intelligence. Despite recent progress toward these goals, there are many open problems in the field that require solutions before many scientific and practical benefits can be realized: Our methods require both conceptual and practical improvements to reveal deeper insights; we must figure out how best to apply our methods in pursuit of specific goals; and the field must grapple with socio-technical challenges that influence and are influenced by our work. This forward-facing review discusses the current frontier of mechanistic interpretability and the open problems that the field may benefit from prioritizing.

This review collects the perspectives of its various authors and represents a synthesis of their views by Apollo Research on behalf of Schmidt Sciences. The perspectives presented here do not necessarily reflect the views of any individual author or the institutions with which they are affiliated.

## 1 Introduction

Recent progress in artificial intelligence (AI) has resulted in rapidly improved AI capabilities. These capabilities are not designed by humans. Instead, they are learned by deep neural networks (Hinton et al., [2006](https://ar5iv.labs.arxiv.org/html/2501.16496#bib.bib185 ""); LeCun et al., [2015](https://ar5iv.labs.arxiv.org/html/2501.16496#bib.bib233 "")). Developers only need to design the training process; they do not need to – and in almost all cases, do not – understand the neural mechanisms underlying the capabilities learned by an AI system.

Although human understanding of these mechanisms is not necessary for AI capabilities, understanding them wo

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