8 papers
SynthSAEBench: Evaluating Sparse Autoencoders on Scalable Realistic Synthetic Data
David Chanin, Adrià Garriga-Alonso
Improving Sparse Autoencoders (SAEs) requires benchmarks that can precisely validate architectural innovations. Current LLM-based SAE benchmarks are too noisy to differentiate arch…
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…
Post-Hoc Reasoning in Chain of Thought: Decoding and Steering Pre-Committed Answers
Kyle Cox, Darius Kianersi, Adrià Garriga-Alonso
As chain of thought (CoT) has become central to scaling reasoning capabilities in large language models (LLMs), it has also emerged as a promising tool for interpretability, sugges…
Among Us: A Sandbox for Measuring and Detecting Agentic Deception
Satvik Golechha, Adrià Garriga-Alonso
Prior studies on deception in language-based AI agents typically assess whether the agent produces a false statement about a topic, or makes a binary choice prompted by a goal, rat…
DiFR: Inference Verification Despite Nondeterminism
Adam Karvonen, Daniel Reuter, Roy Rinberg +3
As demand for LLM inference grows, it is becoming increasingly important that providers and their customers can verify that inference processes are performed correctly, without err…
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…