For every paper on this page, at least one of the original authors has seen our plain-language explanation and engaged with it — either confirming it reads accurately or requesting corrections that we then applied. An endorsement does not mean the authors formally approve every sentence, but it does mean the explanation has passed the eyes of the people who wrote the paper.

438 papers reviewed by authors · 131–140 / 438

A New Framework for Convex Clustering in Kernel Spaces: Finite Sample Bounds, Consistency and Performance Insights

This paper proposes a kernelized convex clustering framework that projects data into a Reproducing Kernel Hilbert Space to effectively handle non-linear and non-convex structures, while providing theoretical guarantees on convergence and finite sample bounds alongside empirical evidence of superior performance over state-of-the-art methods.

Shubhayan Pan, Kushal Bose, Debolina Paul, Saptarshi Chakraborty, Swagatam Das2026-05-15✓ Author reviewed 📊 stat

Age and metallicity of low-mass galaxies: from their centres to their stellar halos

Using 17 simulated low-mass galaxies from the Auriga Project, this study reveals that while stellar metallicity gradients are independent of intrinsic galaxy properties, the dispersion in halo metallicity is driven by satellite accretion timing and the characteristic U-shaped radial age profiles arise from a combination of ceased outer star formation and merger-driven stellar redistribution.

Elisa A. Tau, Antonela Monachesi, Facundo A. Gómez, Robert J. J. Grand, Rüdiger Pakmor, Freeke van de Voort, Federico Marinacci, Rebekka Bieri2026-05-15✓ Author reviewed 🔭 astro-ph

SuperADD: Training-free Class-agnostic Anomaly Segmentation -- CVPR 2026 VAND 4.0 Workshop Challenge Industrial Track

The paper presents SuperADD, a training-free, class-agnostic anomaly segmentation pipeline that leverages a DINOv3 backbone and robust preprocessing techniques to achieve state-of-the-art performance on the MVTec AD 2 dataset under challenging distribution shifts without requiring per-class hyperparameter tuning.

Lukas Roming, Felix Lehnerer, Jonas V. Funk, Andreas Michel, Georg Maier, Thomas Längle, Jürgen Beyerer2026-05-15✓ Author reviewed 💻 cs

Toward Securing AI Agents Like Operating Systems

This paper argues that securing LLM-based AI agents requires applying operating system security principles, demonstrating through a unified architecture analysis and case study that while some risks are inherent, many vulnerabilities can be mitigated using established OS techniques like resource isolation and privilege separation.

Lukas Pirch, Micha Horlboge, Patrick Großmann, Syeda Mahnur Asif, Klim Kireev, Thorsten Holz, Konrad Rieck2026-05-15✓ Author reviewed 💻 cs