collaborators

9 papers

q-bio.GN2026

A mathematical framework for centromere-aware evaluation of human genome assemblies

Luca Franco, Matteo Migliarini, Matteo Tommaso Ungaro +6

Accurate evaluation of genome assemblies within highly repetitive regions, such as centromeres, remains a major open challenge in genomics. Conventional benchmarking relies on sequ…

cs.RO2026

Robotic Policy Adaptation via Weight-Space Meta-Learning

Christian Bianchi, Siamak Yousefi, Alessio Sampieri +4

Vision-Language-Action (VLA) models are emerging as a promising paradigm for robotic manipulation, enabling general-purpose policies trained from large corpora of demonstrations an…

cs.LG2026

CaTS-Bench: Can Language Models Describe Time Series?

Luca Zhou, Pratham Yashwante, Marshall Fisher +4

Time series captioning, the task of describing time series in natural language, requires numeric and temporal reasoning, trend interpretation, and contextual understanding. Existin…

cs.LG2026

Not All Latent Spaces Are Flat: Hyperbolic Concept Control

Maria Rosaria Briglia, Simone Facchiano, Paolo Cursi +6

As modern text-to-image (T2I) models draw closer to synthesizing highly realistic content, the threat of unsafe content generation grows, and it becomes paramount to exercise contr…

cs.AI2026

Quantifying Self-Preservation Bias in Large Language Models

Matteo Migliarini, Joaquin Pereira Pizzini, Luca Moresca +3

Instrumental convergence predicts that sufficiently advanced AI agents will resist shutdown, yet current safety training (RLHF) may obscure this risk by teaching models to deny sel…

cs.CV2026

Video Unlearning via Low-Rank Refusal Vector

Simone Facchiano, Stefano Saravalle, Matteo Migliarini +7

Video generative models achieve high-quality synthesis from natural-language prompts by leveraging large-scale web data. However, this training paradigm inherently exposes them to…