1 citations · 1 across the 7 of their papers we have counts for
9 papers
Evaluating Model Capabilities After Benchmark Saturation
Morio Hamada, Jehyeok Yeon, Matteo Migliarini +2
Rapid advances in AI capabilities are causing benchmarks to saturate. Once several models approach perfect scores, a benchmark loses its power to differentiate between them, and wi…
Prompted Identity Degrades Cooperation in Multi-Agent LLM Systems
Xavier Del Giudice, Alessio Palma, Matteo Migliarini +2
Multi-agent LLM systems increasingly mix models from several providers, yet exposing each agent's underlying model identity to its peers significantly impairs cooperation. We show…
Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks
Nobin Sarwar, Shubhashis Roy Dipta, Zheyuan Liu +1
With the growing adoption of VLMs, DMs, LLMs, and AFMs, these multimodal foundation models can inadvertently encode sensitive, copyrighted, biased, or unsafe cross-modal associatio…
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…
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…
@GrokSet: multi-party Human-LLM Interactions in Social Media
Matteo Migliarini, Berat Ercevik, Oluwagbemike Olowe +5
Large Language Models (LLMs) are increasingly deployed as active participants on public social media platforms, yet their behavior in these unconstrained social environments remain…
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…
Human Motion Unlearning
Edoardo De Matteis, Matteo Migliarini, Alessio Sampieri +2
We introduce Human Motion Unlearning and motivate it through the concrete task of preventing violent 3D motion synthesis, an important safety requirement given that popular text-to…
SeRpEnt: Selective Resampling for Expressive State Space Models
Stefano Rando, Luca Romani, Matteo Migliarini +3
State Space Models (SSMs) have recently enjoyed a rise to prominence in the field of deep learning for sequence modeling, especially as an alternative to Transformers. Their succes…