9 citations · 22 across the 13 of their papers we have counts for
8 papers · 1 filter
LLM Alignment--Utility Asymmetry under Semantic-Preserving Transformations
Mohan Li, Chengyu Yu, Francesco Sovrano +2
Large Language Model (LLM) alignment is intended to ensure that models remain helpful and safe, but its stability under input distributional shift is not yet fully understood. Prio…
Security and Privacy in Agentic AI: Grand Challenges and Future Directions
Adam Jenkins, Agnieszka Kitkowska, Caterina Maidhof +22
We present key challenges and future research directions in the security and privacy of agentic AI, based on a horizon-scanning exercise that brought together thirty leading intern…
SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention
Lidia Losavio, Francesco Sovrano, Dario Fenoglio +2
Money laundering threatens financial stability and exposes institutions to penalties, motivating automated detection. Because laundering schemes often emerge through relational pat…
Neuron-Anchored Rule Extraction for Large Language Models via Contrastive Hierarchical Ablation
Francesco Sovrano, Gabriele Dominici, Marc Langheinrich
A central goal of explainable AI is to express large language model (LLM) decision logic symbolically and ground it in internal mechanisms. Existing rule-extraction methods usually…
Mitigating Prompt-Induced Cognitive Biases in General-Purpose AI for Software Engineering
Francesco Sovrano, Gabriele Dominici, Alberto Bacchelli
Prompt-induced cognitive biases are changes in a general-purpose AI (GPAI) system's decisions caused solely by biased wording in the input (e.g., framing, anchors), not task logic.…
Assessing Model-Agnostic XAI Methods against EU AI Act Explainability Requirements
Francesco Sovrano, Giulia Vilone, Michael Lognoul
Explainable AI (XAI) has evolved in response to expectations and regulations, such as the EU AI Act, which introduces regulatory requirements on AI-powered systems. However, a pers…
Illocutionary Explanation Planning for Source-Faithful Explanations in Retrieval-Augmented Language Models
Francesco Sovrano, Alberto Bacchelli
Natural language explanations produced by large language models (LLMs) are often persuasive, but not necessarily scrutable: users cannot easily verify whether the claims in an expl…
In-Context Symbolic Regression for Robustness-Improved Kolmogorov-Arnold Networks
Francesco Sovrano, Lidia Losavio, Giulia Vilone +1
Symbolic regression aims to replace black-box predictors with concise analytical expressions that can be inspected and validated in scientific machine learning. Kolmogorov-Arnold N…