arXiv:2605.21167v1 Announce Type: cross Abstract: An accurate assessment of a model's complexity is crucial for topics such as interpretation, generalization, and model selection. However, most existing complexity measures either rely on heuristic assumptions or are computationally prohibitive. In this paper, we present a mathematically rigorous yet easy-to-compute measure of model complexity that is based on the similarities between the model gradients across inputs. It is thus well-defined for any parametric model, but also for kernel-based non-parametric models. We prove that our measure of
The rapid development and deployment of increasingly complex AI models necessitate more robust and practical methods for understanding and managing their behavior.
Improved model complexity measures are critical for enhancing AI interpretation, generalization, and reliable model selection, directly impacting the trustworthiness and efficiency of AI systems.
The availability of a 'rigorous, tractable' complexity measure could move model complexity assessment from heuristic guesswork to a more scientific and automatable process.
- · AI developers
- · AI researchers
- · MLOps platforms
- · Regulatory bodies
- · Developers reliant on heuristic complexity measures
This measure will allow for more informed choices in AI model development and deployment.
It could lead to the development of more efficient and generalizable AI models across various applications.
Enhanced understanding of model complexity might facilitate improved safety, interpretability, and compliance features, indirectly accelerating AI adoption in sensitive sectors.
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Read at arXiv cs.LG