Interpretative Interfaces: Designing for AI-Mediated Reading Practices and the Knowledge Commons

ArXi:2603.15863v1 Announce Type: cross Explainable AI (XAI) interfaces seek to make large language models transparent, yet explanation alone does not produce understanding. Explaining a system's behavior is not the same as being able to engage with it, to probe and interpret its operations through direct manipulation. This distinction matters for scientific disciplines in particular: scientists who increasingly rely on LLMs for reading, citing, and producing literature reviews have little means of directly engaging with how these models process and transform the texts they generate.