arXiv:2602.15438v3 Announce Type: replace Abstract: For a broad family of discriminative models that includes autoregressive language models, identifiability results imply that if two models induce the same conditional distributions, then their internal representations are equal up to an invertible linear transformation. We ask whether an analogous conclusion holds approximately when the distributions are close instead of equal. Building on the observation of Nielsen et al. (2025) that closeness in KL divergence need not imply high linear representational similarity, we study a distributional
This paper, published on arXiv, builds on recent research in AI representation, exploring the approximate relationship between close conditional distributions and internal model representations, which is a current frontier in AI interpretability.
For developers and researchers, understanding representational similarity is crucial for evaluating model robustness, transferability, and the implications of using different training data or architectures.
This research refines our understanding of how closely related models are at an internal level even when their outputs are merely 'close' rather than identical, moving beyond previous limitations.
- · AI researchers
- · ML model developers
- · AI interpretability tools
- · Overly simplistic model comparison methodologies
Improved methods for comparing, merging, and evaluating diverse AI models based on their internal structure.
Faster development and deployment of more robust and adaptable AI systems due to better understanding of representation.
Enhanced ability to detect and mitigate biases or vulnerabilities within AI models by scrutinizing their internal representations more effectively.
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Read at arXiv cs.LG