25 citations · 43 across the 30 of their papers we have counts for
7 papers · 1 filter
Adversarial Poetry as a Universal Single-Turn Jailbreak Mechanism in Large Language Models
Piercosma Bisconti, Matteo Prandi, Federico Pierucci +7
We present evidence that adversarial poetry functions as a universal single-turn jailbreak technique for Large Language Models (LLMs). Across 25 frontier proprietary and open-weigh…
Guarding the Guardrails: A Taxonomy-Driven Approach to Jailbreak Detection
Francesco Giarrusso, Olga E. Sorokoletova, Vincenzo Suriani +1
Jailbreaking techniques pose a significant threat to the safety of Large Language Models (LLMs). Existing defenses typically focus on single-turn attacks, lack coverage across lang…
Curriculum-Based Multi-Tier Semantic Exploration via Deep Reinforcement Learning
Abdel Hakim Drid, Vincenzo Suriani, Daniele Nardi +1
Navigating and understanding complex and unknown environments autonomously demands more than just basic perception and movement from embodied agents. Truly effective exploration re…
Multi Robot Coordination in Highly Dynamic Environments: Tackling Asymmetric Obstacles and Limited Communication
Vincenzo Suriani, Daniele Affinita, Domenico D. Bloisi +1
Coordinating a fully distributed multi-agent system (MAS) can be challenging when the communication channel has very limited capabilities in terms of sending rate and packet payloa…
Bench-2-CoP: Can We Trust Benchmarking for EU AI Compliance?
Matteo Prandi, Vincenzo Suriani, Federico Pierucci +3
The rapid advancement of General Purpose AI (GPAI) models necessitates robust evaluation frameworks, especially with emerging regulations like the EU AI Act and its associated Code…
Self-supervised Feature Extraction for Enhanced Ball Detection on Soccer Robots
Can Lin, Daniele Affinita, Marco E. P. Zimmatore +3
Robust and accurate ball detection is a critical component for autonomous humanoid soccer robots, particularly in dynamic and challenging environments such as RoboCup outdoor field…
Context Matters! Relaxing Goals with LLMs for Feasible 3D Scene Planning
Emanuele Musumeci, Michele Brienza, Francesco Argenziano +4
Embodied agents need to plan and act reliably in real and complex 3D environments. Classical planning (e.g., PDDL) offers structure and guarantees, but in practice it fails under n…