Author

Sai Sumedh R. Hindupur

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#artificial intelligence Preprint Sep 2026

When Models Don't Manipulate Manifolds: The Geometry of a Comparison Task

This work characterize precisely the geometry of computation in a number-comparison task, as an abstraction of comparison for decision making, and how models utilize geometry in an elegant fashion to implement it, demonstrating that the manifold hypothesis can co-exist with linear representations.

Sai Sumedh R. Hindupur, Hadas Orgad, Thomas Fel et al. · 0 citations

#artificial intelligence Nov 2025

Sparse, self-organizing ensembles of local kernels detect rare statistical anomalies

This work introduces SparKer, a sparse ensemble of Gaussian kernels trained in a semi-supervised Neyman-Pearson framework to locally model the likelihood ratio between a sample that may contain anomalies and an anomaly-free reference, and provides theoretical insights into the mechanisms driving detection and self-orga...

Gaia Grosso, Sai Sumedh R. Hindupur, Thomas Fel et al. · arXiv.org · 2 citations