arXiv:2605.26973v1 Announce Type: cross Abstract: Neural networks are known to develop latent representations that are $aligned$, namely structurally similar across networks trained with different architectures, training protocols, or training datasets. We study this phenomenon in a controlled setting, where we train an ensemble of networks on regression and classification tasks using training sets perturbed by independent realizations of a noise process. We show that the signal-to-noise ratio (SNR) and the training sample size influence the alignment in qualitatively similar ways in networks
The proliferation of complex AI systems necessitates a deeper understanding of their internal mechanisms and reliability under varying conditions.
Understanding representational alignment in neural networks is crucial for building more robust, generalizable, and auditable AI systems, impacting their real-world deployment.
This research provides a more controlled, quantitative framework for predicting and managing how neural networks form internal representations, offering insights for improved AI design and training methodologies.
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Improved understanding of neural network robustness and generalization.
Development of more principled methods for evaluating and comparing different AI models and architectures.
Enhanced ability to engineer AI systems with predictable and explainable internal states, accelerating AI adoption in critical applications.
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