arXiv:2605.22679v1 Announce Type: cross Abstract: Vision-language models learn powerful multimodal embeddings, yet their internal semantics remain opaque. While sparse autoencoders (SAEs) can extract interpretable features, they rely on expanding the representation dimension, which compromises the original geometry and introduces redundancy. We introduce CEDAR (Conceptual Embedding Disentanglement via Adaptive Rotation), a post-hoc method that reveals the compositional structure of pretrained embeddings without increasing dimensionality. By learning an invertible transformation with a top-$k$
The rapid advancement of large vision-language models necessitates improved methods for interpretability and efficiency, driving innovation in post-hoc analysis techniques.
Improved interpretability of vision-language models will accelerate their development, deployment, and trustworthiness in critical applications, reducing black-box risks.
The ability to understand and refine the internal representations of multimodal AI models without compromising their original performance is enhanced.
- · AI researchers
- · Developers of multimodal AI applications
- · Sectors reliant on AI interpretability (e.g., healthcare, finance)
- · Developers of opaque black-box AI systems
- · Previous less efficient interpretability methods
More efficient and understandable AI models will emerge, leading to faster development cycles.
Increased trust and adoption of advanced AI systems across various industries due to explainability.
The development of new AI architectures that are inherently more interpretable from the outset, reducing reliance on post-hoc methods.
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Read at arXiv cs.LG