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cs.LG2026
Controllable Image Generation with Composed Parallel Token Prediction
Jamie Stirling, Noura Al-Moubayed, Chris G. Willcocks +1
Conditional discrete generative models struggle to faithfully compose multiple input conditions. To address this, we derive a theoretically-grounded formulation for composing discr…
cs.LG2025
Inference-Time Decomposition of Activations (ITDA): A Scalable Approach to Interpreting Large Language Models
Patrick Leask, Neel Nanda, Noura Al Moubayed
Sparse autoencoders (SAEs) are a popular method for decomposing Large Langage Models (LLM) activations into interpretable latents. However, due to their substantial training cost,…
cs.LG2025
Sparse Autoencoders Do Not Find Canonical Units of Analysis
Patrick Leask, Bart Bussmann, Michael Pearce +5
A common goal of mechanistic interpretability is to decompose the activations of neural networks into features: interpretable properties of the input computed by the model. Sparse…