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Background: Multi-modal logic has found many applications in computer science, also providing a framework to model temporal and spatial information. Reasoning in this logic is crucial in many real-world applications from different fields.
This paper investigates how popular text-to-image (T2I) models, DALL-E 3 and Gemini 3 Pro Preview, depict people from 206 nationalities when prompted to generate images of individuals engaging in common everyday activities.
This paper explores knowledge distillation (KD) of multilingual pre-trained encoder-decoder and large language models for machine translation (MT). We argue that the teacher model's output distribution holds valuable insights for the student, beyond the approximated mode obtained through beam…
Background: Clustering of nodes in Bayesian Networks (BNs) and related graphical models such as Dynamic BNs (DBNs) has been demonstrated to enhance computational efficiency and improve model learning.
Neural networks find applications in many safety-critical systems that raise concerns about their deployment: Are we sure they never advise doing anything catastrophic?
Background: Modern SAT solvers are indispensable reasoning tools that are widely applied in verification, planning, and combinatorial design. Beyond answering satisfiability, they also produce unsatisfiability proofs, which are valuable for certification and analysis.
Text generation has become more accessible than ever, and the growing interest in these systems, especially those using large language models, has spurred a surge in related publications.
Background: Hierarchical Classification (HC) has long been recognized for improving predictive performance by exploiting relationships between classes. However, most tabular multi-class datasets lack predefined class hierarchies, limiting the broader applicability of hierarchy-aware learning…
Neuro-symbolic (NeSy) AI aims to develop deep neural networks whose predictions comply with prior knowledge encoding, e.g., safety or structural constraints. As such, it represents one of the most promising avenues for reliable and trustworthy AI.
Background: Deep neural networks increasingly power language, vision, and decision systems, yet many deployments require explanations that are faithful, compositional, and governance-ready.
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