4 papers
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…
Path Channels and Plan Extension Kernels: a Mechanistic Description of Planning in a Sokoban RNN
Mohammad Taufeeque, Aaron David Tucker, Adam Gleave +1
We partially reverse-engineer a convolutional recurrent neural network (RNN) trained with model-free reinforcement learning to play the box-pushing game Sokoban. We find that the R…
Planning in a recurrent neural network that plays Sokoban
Mohammad Taufeeque, Philip Quirke, Maximilian Li +4
Planning is essential for solving complex tasks, yet the internal mechanisms underlying planning in neural networks remain poorly understood. Building on prior work, we analyze a r…
Codebook Features: Sparse and Discrete Interpretability for Neural Networks
Alex Tamkin, Mohammad Taufeeque, Noah D. Goodman
Understanding neural networks is challenging in part because of the dense, continuous nature of their hidden states. We explore whether we can train neural networks to have hidden…