activity
20102026
most citedA Roadmap Towards Resilient Internet of Things for Cyber-Physical Systems

137 citations · 525 across the 67 of their papers we have counts for

collaborators

76 papers

cs.RO2026

RoboRacer Arena: Specification-Driven Track Construction for Autonomous Racing

Mihaela-Larisa Clement, Agnes Poks, Ezio Bartocci

Learning-based autonomous racing relies on diverse training environments, yet constructing a new track requires geometric design, validation, and simulation-asset generation. This…

cs.SE2026

ATLAS: Discovering Agent Strategies through LLM-Guided Abstraction and Automata Learning

Ignacio D. Lopez-Miguel, Andreas Happe, Jürgen Cito +3

Large Language Model (LLM)-based agents are increasingly used for complex tasks such as software testing and cybersecurity assessment. While these agents demonstrate impressive cap…

cs.LO2026

STLSat---An Improved Tableau for Satisfiability Checking of Signal Temporal Logic Formulas

Marco Zamponi, Florian Lammel, Ezio Bartocci +1

Signal Temporal Logic (STL) is a formalism used to describe temporal properties of real-valued signals in cyber-physical systems. In mission- and safety-critical domains, specifica…

cs.LG2026

ORCAID: Oblique Rule-Based Continuous-Action Interpretation for Deep RL Policies

Ignacio D. Lopez-Miguel, Ezio Bartocci, Thomas Eiter +1

Explainability remains a key issue in reinforcement learning (RL). Distilling an interpretable policy from an agent trained in a complex environment is particularly challenging whe…

cs.AI2026

A Survey on the Verification of Reinforcement Learning Policies

Luca Marzari, Ezio Bartocci, Enrico Marchesini

Reinforcement learning (RL) is increasingly applied in complex, safety-critical domains, yet the lack of rigorous behavioral guarantees for neural network-based policies remains a…

cs.CR2026

Differential Zonotopes for Verifying Global Robustness of DNNs

Anagha Athavale, Samuel Teuber, Matteo Maffei +3

The robustness of deep neural networks (DNNs) is critical in security-sensitive applications, where small input perturbations should not alter model predictions. This property is c…