Title: Atlas-Alignment: Making Interpretability Transferable Across Language Models
Primary area: interpretability and explainable AI
Keywords: mechanistic interpretability, interpretability, explainable AI, representational alignment, representational similarity, LLM

Abstract: Interpretability is crucial for building safe, reliable, and controllable language models, yet existing interpretability pipelines remain costly and difficult to scale. Interpreting a new model typically requires costly training of model-specific sparse autoencoders, manual or semi-automated labeling of SAE components, and their subsequent validation. We introduce Atlas-Alignment, a framework for transferring interpretability across language models by aligning unknown latent spaces to a Concept Atlas — a labeled, human-interpretable latent space — using only shared inputs and lightweight representational alignment techniques. Once aligned, this enables two key capabilities in previously opaque models: (1) semantic feature search and retrieval, and (2) steering generation along human-interpretable atlas concepts. Through quantitative and qualitative evaluations, we show that simple representational alignment methods enable robust semantic retrieval and steerable generation without the need for labeled concept data. Atlas-Alignment thus amortizes the cost of explainable AI and mechanistic interpretability: by investing in one high-quality Concept Atlas, we can make many new models transparent and controllable at minimal marginal cost.

TL;DR: We introduce Atlas-Alignment, a framework for transferring interpretability across language models by aligning unknown latent spaces to a labeled, human-interpretable latent space.