Charting LLM Embedding Spaces with Explainable Mapper

We apply mapper graphs—a widely used tool in topological data analysis and visualization—to investigate the topological structures of large language model (LLM) embedding spaces. The mapper’s taxonomy includes elements such as nodes, edges, paths, components, and trajectories. We introduce the Explainable Mapper workspace and two mapper agents to support embedding investigation. These agents utilize summarization, comparison, and perturbation operations to generate and verify explanations of mapper elements such as the linguistic aspect of clusters, connectivity, and transitions. This is a joint work involving Xinyuan Yan, Rita Sevastjanova, Sinie van der Ben, Mennatallah El-Assady, and Bei Wang. arxiv.org/abs/2507.18607