184 citations · 489 across the 31 of their papers we have counts for
19 papers · 1 filter
Compressed Computation under Loss is likely Computation in Superposition
Francisco Ferreira da Silva, Stefan Heimersheim
Neural networks are thought to represent concepts as directions in their activation space, and superposition lets them encode more concepts than they have dimensions. It is natural…
Individual Parameters in Weight-Sparse Transformers Appear Interpretable
Arnau Marin-Llobet, Stefan Heimersheim
A central goal of mechanistic interpretability is to understand how neural networks work and what each individual component does. Dominant circuit-finding approaches focus on a spe…
The Model Organism Lottery: Model Organism Interpretability Strongly Depends on Training Methodology
Andrzej Szablewski, Gabriel Konar-Steenberg, Raffaello Fornasiere +2
Model organisms (MOs) - language models trained to exhibit undesired or unnatural behaviours - are frequently used as testbeds for evaluating white-box interpretability techniques.…
Evidence for feature-specific error correction in LLMs
Francisco Ferreira da Silva, Stefan Heimersheim
Understanding the features of large language models (LLMs) is a central goal of interpretability. LLMs are commonly assumed to use superposition to represent more features than the…
Compressed Computation is (probably) not Computation in Superposition
Jai Bhagat, Sara Molas-Medina, Giorgi Giglemiani +1
We study whether the Compressed Computation (CC) toy model (Braun et al., 2025) is an instance of computation in superposition. The CC model appears to compute 100 ReLU functions w…
The Obfuscation Atlas: Mapping Where Honesty Emerges in RLVR with Deception Probes
Mohammad Taufeeque, Stefan Heimersheim, Adam Gleave +1
Training against white-box deception detectors has been proposed as a way to make AI systems honest. However, such training risks models learning to obfuscate their deception to ev…
SCALAR: Benchmarking SAE Interaction Sparsity in Toy LLMs
Sean P. Fillingham, Andrew Gordon, Peter Lai +3
Mechanistic interpretability aims to decompose neural networks into interpretable features and map their connecting circuits. The standard approach trains sparse autoencoders (SAEs…
Transformers Don't Need LayerNorm at Inference Time: Scaling LayerNorm Removal to GPT-2 XL and the Implications for Mechanistic Interpretability
Luca Baroni, Galvin Khara, Joachim Schaeffer +2
Layer-wise normalization (LN) is an essential component of virtually all transformer-based large language models. While its effects on training stability are well documented, its r…
Interpretability in Parameter Space: Minimizing Mechanistic Description Length with Attribution-based Parameter Decomposition
Dan Braun, Lucius Bushnaq, Stefan Heimersheim +2
Mechanistic interpretability aims to understand the internal mechanisms learned by neural networks. Despite recent progress toward this goal, it remains unclear how best to decompo…
Detecting Strategic Deception Using Linear Probes
Nicholas Goldowsky-Dill, Bilal Chughtai, Stefan Heimersheim +1
AI models might use deceptive strategies as part of scheming or misaligned behaviour. Monitoring outputs alone is insufficient, since the AI might produce seemingly benign outputs…
Open Problems in Mechanistic Interpretability
Lee Sharkey, Bilal Chughtai, Joshua Batson +26
Mechanistic interpretability aims to understand the computational mechanisms underlying neural networks' capabilities in order to accomplish concrete scientific and engineering goa…
Investigating Sensitive Directions in GPT-2: An Improved Baseline and Comparative Analysis of SAEs
Daniel J. Lee, Stefan Heimersheim
Sensitive directions experiments attempt to understand the computational features of Language Models (LMs) by measuring how much the next token prediction probabilities change by p…
Evolution of SAE Features Across Layers in LLMs
Daniel Balcells, Benjamin Lerner, Michael Oesterle +2
Sparse Autoencoders for transformer-based language models are typically defined independently per layer. In this work we analyze statistical relationships between features in adjac…
Characterizing stable regions in the residual stream of LLMs
Jett Janiak, Jacek Karwowski, Chatrik Singh Mangat +3
We identify stable regions in the residual stream of Transformers, where the model's output remains insensitive to small activation changes, but exhibits high sensitivity at region…
Evaluating Synthetic Activations composed of SAE Latents in GPT-2
Giorgi Giglemiani, Nora Petrova, Chatrik Singh Mangat +2
Sparse Auto-Encoders (SAEs) are commonly employed in mechanistic interpretability to decompose the residual stream into monosemantic SAE latents. Recent work demonstrates that pert…
The Local Interaction Basis: Identifying Computationally-Relevant and Sparsely Interacting Features in Neural Networks
Lucius Bushnaq, Stefan Heimersheim, Nicholas Goldowsky-Dill +7
Mechanistic interpretability aims to understand the behavior of neural networks by reverse-engineering their internal computations. However, current methods struggle to find clear…
Using Degeneracy in the Loss Landscape for Mechanistic Interpretability
Lucius Bushnaq, Jake Mendel, Stefan Heimersheim +5
Mechanistic Interpretability aims to reverse engineer the algorithms implemented by neural networks by studying their weights and activations. An obstacle to reverse engineering ne…
How to use and interpret activation patching
Stefan Heimersheim, Neel Nanda
Activation patching is a popular mechanistic interpretability technique, but has many subtleties regarding how it is applied and how one may interpret the results. We provide a sum…
Towards Automated Circuit Discovery for Mechanistic Interpretability
Arthur Conmy, Augustine N. Mavor-Parker, Aengus Lynch +2
Through considerable effort and intuition, several recent works have reverse-engineered nontrivial behaviors of transformer models. This paper systematizes the mechanistic interpre…