8 citations · 13 across the 8 of their papers we have counts for
10 papers
Reflections and Fragments: Securing LLMs Against Sequential Mosaic Attacks
Emanuele La Malfa, Saar Cohen, Gabriele La Malfa +4
Self-play red-teaming improves language-model safety by pitting attacker and defender roles against each other in a zero-sum game. However, real adversaries increasingly use mosaic…
Explore, Map, Remember, Decide: Are Embodied VLMs Ready for Safety-Critical Scenarios?
Gabriele La Malfa, Nitay Alon, Emanuele La Malfa +2
Theory of Space framework (ToS) assesses the spatial understanding of curiosity-driven Vision-Language Models (VLMs) under partial observability. As AI techniques are increasingly…
The Attacker in the Mirror: Breaking Self-Consistency in Safety via Anchored Bipolicy Self-Play
Gabriele La Malfa, Emanuele La Malfa, Saar Cohen +4
Self-play red team is an established approach to improving AI safety in which different instances of the same model play attacker and defender roles in a zero-sum game, i.e., where…
Large Language Models Miss the Multi-Agent Mark
Emanuele La Malfa, Gabriele La Malfa, Samuele Marro +5
Recent interest in Multi-Agent Systems of Large Language Models (MAS LLMs) has led to an increase in frameworks leveraging multiple LLMs to tackle complex tasks. However, much of t…
Tracing the ejecta structure of SN 1987A: Insights and diagnostics from 3D MHD simulations
S. Orlando, M. Miceli, M. Ono +14
Supernova (SN) 1987A provides a unique window into the aftermath of a massive stellar explosion, offering key insights into the ejecta's morphology, composition, explosion mechanis…
Fairness Aware Reinforcement Learning via Proximal Policy Optimization
Gabriele La Malfa, Jie M. Zhang, Michael Luck +1
Fairness in multi-agent systems (MAS) focuses on equitable reward distribution among agents in scenarios involving sensitive attributes such as race, gender, or socioeconomic statu…
Using Protected Attributes to Consider Fairness in Multi-Agent Systems
Gabriele La Malfa, Jie M. Zhang, Michael Luck +1
Fairness in Multi-Agent Systems (MAS) has been extensively studied, particularly in reward distribution among agents in scenarios such as goods allocation, resource division, lotte…
Deep Neural Networks via Complex Network Theory: a Perspective
Emanuele La Malfa, Gabriele La Malfa, Giuseppe Nicosia +1
Deep Neural Networks (DNNs) can be represented as graphs whose links and vertices iteratively process data and solve tasks sub-optimally. Complex Network Theory (CNT), merging stat…
Deep Neural Networks as Complex Networks
Emanuele La Malfa, Gabriele La Malfa, Claudio Caprioli +2
Deep Neural Networks are, from a physical perspective, graphs whose `links` and `vertices` iteratively process data and solve tasks sub-optimally. We use Complex Network Theory (CN…
Characterizing Learning Dynamics of Deep Neural Networks via Complex Networks
Emanuele La Malfa, Gabriele La Malfa, Giuseppe Nicosia +1
In this paper, we interpret Deep Neural Networks with Complex Network Theory. Complex Network Theory (CNT) represents Deep Neural Networks (DNNs) as directed weighted graphs to stu…