12 papers
Co-Recursive Semantic Self-Extension: A Mathematical Foundation for Open-Ended Intelligence
Simon Hegelich, Bardh Prenkaj, Habiba Sarhan +5
Artificial intelligence has advanced through scale, data, and computation, yet remains bounded by its representational framework. We ask what makes intelligence open-ended. Every i…
Causal Routing for Unlearning
Bardh Prenkaj, Andrea D'Angelo, Davide Mottin +4
LLMs cannot forget the way we delete a file. Strangely, we are asked to remove something that was never put anywhere in particular. What the model took from a piece of text is now…
High-Dimensional Online Change Point Detection with Adaptive Thresholding and Interpretability
Sven Jacob, Bardh Prenkaj, Weijia Shao +1
Change point detection (CPD) identifies abrupt and significant changes in sequential data, with applications in human activity recognition, financial markets, cybersecurity, manufa…
Beyond Binary Rewards: A Comparative Study of Reward Design for Reinforcement Unlearning
Efstratios Zaradoukas, Davide Gabrielli, Bardh Prenkaj +1
Machine unlearning seeks to selectively remove specific knowledge from trained language models without full retraining, a growing necessity under privacy regulations such as GDPR a…
Active Tabular Augmentation via Policy-Guided Diffusion Inpainting
Zheyu Zhang, Shuo Yang, Bardh Prenkaj +1
Generative tabular augmentation is appealing in data-scarce domains, yet the prevailing focus on distributional fidelity does not reliably translate into better downstream models.…
SAGE: Sparse Adaptive Guidance for Dependency-Aware Tabular Data Generation
Shuo Yang, Zheyu Zhang, Bardh Prenkaj +1
Generating high-fidelity synthetic tabular data remains a critical challenge for enhancing data availability in privacy-sensitive and low-resource domains. Recent approaches levera…
TabSCM: A practical Framework for Generating Realistic Tabular Data
Sven Jacob, Bardh Prenkaj, Weijia Shao +1
Most tabular-data generators match marginal statistics yet ignore causal structure, leading downstream models to learn spurious or unfair patterns. We present TabSCM, a mixed-type…
Analysing the Safety Pitfalls of Steering Vectors
Yuxiao Li, Alina Fastowski, Efstratios Zaradoukas +2
Activation steering has emerged as a powerful tool to shape LLM behavior without the need for weight updates. While its inherent brittleness and unreliability are well-documented,…
Beyond Edge Deletion: A Comprehensive Approach to Counterfactual Explanation in Graph Neural Networks
Matteo De Sanctis, Riccardo De Sanctis, Stefano Faralli +2
Graph Neural Networks (GNNs) are increasingly adopted across domains such as molecular biology and social network analysis, yet their black-box nature hinders interpretability and…
A Multi-Agent Framework for Interpreting Multivariate Physiological Time Series
Davide Gabrielli, Paola Velardi, Stefano Faralli +1
Continuous physiological monitoring is central to emergency care, yet deploying trustworthy AI is challenging. While LLMs can translate complex physiological signals into clinical…
Moral Lenses, Political Coordinates: Towards Ideological Positioning of Morally Conditioned LLMs
Chenchen Yuan, Bolei Ma, Zheyu Zhang +3
While recent research has systematically documented political orientation in large language models (LLMs), existing evaluations rely primarily on direct probing or demographic pers…
Reinforcement Unlearning via Group Relative Policy Optimization
Efstratios Zaradoukas, Bardh Prenkaj, Gjergji Kasneci
During pretraining, LLMs inadvertently memorize sensitive or copyrighted data, posing significant compliance challenges under legal frameworks like the GDPR and the EU AI Act. Fulf…