arXiv:2512.07355v2 Announce Type: replace-cross Abstract: Two traditions of interpretability have evolved side by side but seldom spoken to each other: Concept Bottleneck Models (CBMs), which prescribe what a concept should be, and Sparse Autoencoders (SAEs), which discover what concepts emerge. While CBMs use supervision to align activations with human-labeled concepts, SAEs rely on sparse coding to uncover emergent ones. We show that both paradigms instantiate the same geometric structure: each learns a set of linear directions in activation space whose nonnegative combinations form a concep
This research builds on existing interpretability methods, CBMs and SAEs, reflecting a growing industry push towards more transparent and controllable AI systems.
Unified interpretability frameworks can accelerate the development of reliable and deployable AI, addressing key safety and ethical concerns necessary for broader adoption.
The perceived distinction between CBMs and SAEs diminishes, allowing for more integrated and potentially more powerful approaches to AI interpretability.
- · AI Safety Researchers
- · AI Development Platforms
- · High-Stakes AI Applications (e.g., medical, finance)
- · Regulatory Bodies
- · Black Box AI Models
- · Organizations with Poor AI Governance
Improved interpretability leads to more robust and less error-prone AI systems.
Increased trust in AI drives faster adoption and deeper integration into critical societal functions.
More explainable AI facilitates regulatory clarity, potentially accelerating AI development while ensuring public safety.
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