most citedThe Geometry of Representational Failures in Vision Language Models

1 citations · 1 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CV20261 cited

The Geometry of Representational Failures in Vision Language Models

Daniele Savietto, Declan Campbell, André Panisson +4

Vision-Language Models (VLMs) exhibit puzzling failures in multi-object visual tasks, such as hallucinating non-existent elements or failing to identify the most similar objects am…

cond-mat.stat-mech2026

Harmonic morphisms and dynamical invariants in network renormalization

Francesco Maria Guadagnuolo, Marco Nurisso, Federica Galluzzi +2

Renormalization of complex networks requires principled criteria for assessing whether a coarse-graining preserves dynamical content. We prove that discrete harmonic morphisms -- s…

cs.LG2026

Topology and Geometry of the Learning Space of ReLU Networks: Connectivity and Singularities

Marco Nurisso, Pierrick Leroy, Giovanni Petri +1

Understanding the properties of the parameter space in feed-forward ReLU networks is critical for effectively analyzing and guiding training dynamics. After initialization, trainin…

eess.SP2025

From Nodes to Edges: Edge-Based Laplacians for Brain Signal Processing

Andrea Santoro, Marco Nurisso, Giovanni Petri

Traditional graph signal processing (GSP) methods applied to brain networks focus on signals defined on the nodes. Thus, they are unable to capture potentially important dynamics o…

cs.LG2025

Bound by semanticity: universal laws governing the generalization-identification tradeoff

Marco Nurisso, Jesseba Fernando, Raj Deshpande +9

Intelligent systems must deploy internal representations that are simultaneously structured -- to support broad generalization -- and selective -- to preserve input identity. We ex…