arXiv:2601.21568v2 Announce Type: replace Abstract: We present a unified framework for quantifying the similarity between representations through the lens of \textit{usable} information, offering a rigorous theoretical and empirical synthesis across three key dimensions. First, addressing functional similarity, we establish a formal link between stitching performance and conditional mutual information. We further reveal that stitching is inherently asymmetric, demonstrating that robust functional comparison necessitates a bidirectional analysis rather than a unidirectional mapping. Second, con
The proliferation of AI models and architectures necessitates more robust and reliable methods for comparing and understanding their internal workings and representational capabilities.
A unified framework for quantifying representational similarity is crucial for advancing AI development, enabling better model selection, explainability, and the transfer of learning across different systems.
This research provides a more rigorous theoretical and empirical foundation for evaluating AI models, moving beyond superficial metrics to deeper functional and representational comparisons.
- · AI researchers
- · Machine learning explainability platforms
- · Deep learning model developers
- · Ad-hoc AI comparison methods
- · Systems relying solely on superficial performance metrics
Improved methods for comparing, contrasting, and understanding complex AI models will accelerate research in areas like causal inference and interpretability.
This could lead to more efficient development of specialized AI agents and systems, as it becomes easier to assess suitability and transferability of learned representations.
Ultimately, a clearer understanding of AI representations could contribute to more robust and trustworthy AI applications in critical domains, reducing the black-box nature of current systems.
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Read at arXiv cs.LG