9 citations · 10 across the 20 of their papers we have counts for
9 papers · 1 filter
Interpretability for Turing Machines
Billy Snikkers, Rumi Salazar, Daniel Murfet +1
We show that susceptibilities, an interpretability technique developed for neural networks, can identify the presence of algorithmic structure in Turing machines by probing the loc…
Patterning in Practice: Debiasing Reward Models with Susceptibilities
George Wang, Elizabeth Donoway, Daniel Murfet
Reward models trained on human preferences are known to suffer from length, formatting, and other stylistic biases. In this paper we use patterning, which reweights each preference…
Elicitation without Backpropagation: Steering Model Behavior by Optimizing the Latent Posterior
Garrett Baker, Vinayak Pathak, Daniel Murfet +1
In the \emph{latent posterior model} of transformer behavior, the next-token distribution arises from a posterior over latent predictive models conditioned on the context, mixed to…
Interpreting Reinforcement Learning Agents with Susceptibilities
Chris Elliott, Einar Urdshals, David Quarel +1
Susceptibilities are a technique for neural network interpretability that studies the response of posterior expectation values of observables to perturbations of the loss. We gener…
Susceptibilities and Patterning: A Primer on Linear Response in Bayesian Learning
Chris Elliott, Daniel Murfet
These notes introduce the theory of susceptibilities as developed in [arXiv:2504.18274, arXiv:2601.12703] for interpreting neural networks. The susceptibility of an observable …
Linear Response Estimators for Singular Statistical Models
Chris Elliott, Daniel Murfet
We define susceptibilities as a measure of the response of an observable quantity of a parameterized statistical model to a perturbation of the data for a general class of observab…
Stagewise Reinforcement Learning and the Geometry of the Regret Landscape
Chris Elliott, Einar Urdshals, David Quarel +2
Singular learning theory characterizes Bayesian learning as an evolving tradeoff between accuracy and complexity, with transitions between qualitatively different solutions as samp…
Patterning: The Dual of Interpretability
George Wang, Daniel Murfet
Mechanistic interpretability aims to understand how neural networks generalize beyond their training data by reverse-engineering their internal structures. We introduce patterning…
Towards Spectroscopy: Susceptibility Clusters in Language Models
Andrew Gordon, Garrett Baker, George Wang +3
Spectroscopy infers the internal structure of physical systems by measuring their response to perturbations. We apply this principle to neural networks: perturbing the data distrib…