Publication
What's in the Bottle? A Survey and Roadmap of Concept Bottleneck Models
Patrick Knab; David Steinmann; Christian Bartelt; Kristian Kersting; Bernt Schiele; Thomas Seidl; Rudolf Schlegel; Wolfgang Stammer
In: Transactions on Machine Learning Research (TMLR), Vol. 2026, Pages 1-38, Open Review, 2026.
Abstract
Concept Bottleneck Models (CBMs) are interpretable learning architectures that factor
predictions through intermediate, ideally human-understandable concepts, enabling explicit
and inspectable reasoning. Although CBM research has gained substantial momentum in
recent years, this growth has also revealed numerous open challenges and a fragmented set of
methodological choices. In this work, we systematically review the CBM literature, identify
previously unidentified core components and challenges, and propose a unified taxonomy.
Based on this taxonomy, we provide a detailed categorization of existing works. We hereby
discuss current challenges for the CBM paradigm and outline important directions to extend
it beyond its current scope. Overall, this survey aims to consolidate the CBM landscape,
clarify open issues, and provide guidance for developing future models