arXiv:2505.20142v2 Announce Type: replace Abstract: In deep learning, functional similarity evaluation quantifies the extent to which independently trained models learn similar input--output relationships. In model stitching, functional similarity is framed as representation forward compatibility, i.e., whether the representations of two models can be aligned to solve a given task. Recent studies, however, highlight a critical limitation: models relying on different information cues can still produce compatible representations, making them appear misleadingly similar (Smith et al., 2025). We a
The paper addresses a critical limitation in evaluating functional similarity in deep learning, a problem exposed by recent studies highlighting potential misinterpretations of model behavior.
Improving the accuracy of functional similarity evaluation is crucial for the reliable development and deployment of complex AI systems, impacting interpretability, transfer learning, and model integration.
The proposed 'invariance-aware model stitching' method offers a more robust way to assess how similar independently trained AI models truly are in their learned behaviors, moving beyond superficial compatibility.
- · AI researchers
- · Deep learning practitioners
- · Model developers
- · AI safety researchers
- · Developers relying on simplistic model comparison metrics
More accurate assessment of AI model commonalities and differences.
Improved ability to combine and transfer learning between distinct AI models without unexpected failures.
Accelerated development of generalizable AI, as models can be more effectively built upon and integrated.
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