Hierarchical, Interpretable, Label-Free Concept Bottleneck Model
Concept Bottleneck Models (CBMs) introduce interpretability to black-box deep learning models by predicting labels through human-understandable concepts. However, unlike humans, who identify objects at different levels of abstraction using both general and specific features, existing CBMs operate at a single semantic level in both concept and label space. We propose HIL-CBM, a Hierarchical Interpretable Label-Free Concept Bottleneck Model that extends CBMs into a hierarchical framework to enhanc
Record details
Published: 2 April 2026
Source: arXiv
Category: Research
Topics: Safety & alignment
Retrieved: 14 July 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
Understanding the Effects of Safety Unalignment on Large Language Models
arXiv · 2 April 2026
Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning
arXiv · 2 April 2026
Do Audio-Visual Large Language Models Really See and Hear?
arXiv · 3 April 2026
Woosh: A Sound Effects Foundation Model
arXiv · 2 April 2026
Generalization Limits of Reinforcement Learning Alignment
arXiv · 3 April 2026
DocShield: Towards AI Document Safety via Evidence-Grounded Agentic Reasoning
arXiv · 3 April 2026
How to cite this record
ethics.ai (2 April 2026), “Hierarchical, Interpretable, Label-Free Concept Bottleneck Model,” evidence record 6433, https://ethics.ai/record/6433 (originally published by arXiv).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.