Sid Black*, Lee Sharkey*, Leo Grinsztajn, Eric Winsor, Dan Braun, Jacob Merizian, Kip Parker, Carlos Ramón Guevara, Beren Millidge, Gabriel Alfour, Connor Leahy *equal contribution Research from Conjecture. This post benefited from feedback from many staff at Conjecture including Adam Shimi, Nicholas Kees Dupuis, Dan Clothiaux, Kyle McDonell. Additionally, the post also benefited from inputs from Jessica Cooper, Eliezer Yudkowsky, Neel Nanda, Andrei Alexandru, Ethan Perez, Jan Hendrik Kirchner, Chris Olah, Nelson Elhage, David Lindner, Evan R Murphy, Tom McGrath, Martin Wattenberg, Johannes Treutlein, Spencer Becker-Kahn, Leo Gao, John Wentworth, and Paul Christiano and from discussions with many other colleagues working on interpretability. Summary Mechanistic interpretability aims to explain what a neural network has learned at a nuts-and-bolts level. What are the fundamental primitives of neural network representations? What basic objects should we use to describe the operation of neural networks mechanistically? Previous mechanistic descriptions have used individual neurons or their linear combinations to understand the representations a network has learned. But there are clues that neurons and their linear combinations are not the correct fundamental units of description - directions cannot describe how neural networks use nonlinearities to structure their representations. Moreover, many instances of individual neurons and their combinations are polysemantic (i.e. they have multiple unrelated meanings). Polysemanticity makes interpreting the network in terms of neurons or directions challenging since we can no longer assign a specific feature to a neural unit. In order to find a basic unit of description that doesn’t suffer from these problems, we zoom in beyond just directions to study the way that piecewise linear activation functions (such as ReLU) partition the activation space into numerous discrete polytopes. We call this perspective the ‘