arXiv:2606.09653v1 Announce Type: new Abstract: Learned representations across models and modalities often exhibit striking structural similarities, suggesting shared underlying concept decompositions. However, concept alignment remains poorly defined: existing approaches optimize different objectives under the same terminology, obscuring what is actually aligned. We propose a unifying framework that decomposes alignment along two axes: what is aligned (representations vs. concepts) and at what level (instance-wise vs. distributional). This induces four corresponding properties -- instance-wis
This paper proposes a new unifying framework for understanding concept alignment in AI, addressing a foundational challenge as AI models become more complex and interdependent.
A clearer, unified understanding of how AI models represent and align concepts is critical for advancing AI interpretability, transfer learning, and the development of more robust, scalable, and trustworthy AI systems.
The proposed framework provides a standardized language and decomposition for analyzing concept alignment, moving towards more systematic and rigorous research in representational similarity.
- · AI researchers
- · Developers of foundational models
- · AI interpretability tools
- · Ad-hoc AI comparison methods
- · Unstandardized AI evaluation
Improved methods for evaluating and comparing learned representations across diverse AI models and modalities.
Accelerated development of more generalizable and transferable AI models by enabling more effective concept alignment.
Enhanced ability to diagnose and mitigate biases or security vulnerabilities within complex AI systems by understanding core concept representations.
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Read at arXiv cs.LG