7 papers
Think it, Run it: Autonomous ML pipeline generation via self-healing multi-agent AI
Adela Bara, Gabriela Dobrita, Simona-Vasilica Oprea
The purpose of our paper is to develop a unified multi-agent architecture that automates end-to-end machine learning (ML) pipeline generation from datasets and natural-language (NL…
A phenotype-driven and evidence-governed framework for knowledge graph enrichment and hypotheses discovery in population data
Adela Bâra, Simona-Vasilica Oprea
Current knowledge graph (KG) construction methods are confirmatory, focusing on recovering known relationships rather than identifying novel or context-dependent nodes. This paper…
Are we still able to recognize pearls? Machine-driven peer review and the risk to creativity: An explainable RAG-XAI detection framework with markers extraction
Alin-Gabriel Văduva, Simona-Vasilica Oprea, Adela Bâra
The integration of large language models (LLMs) into peer review raises a concern beyond authorship and detection: the potential cascading automation of the entire editorial proces…
Generative-AI and the transformation of workforce. A job postings-driven analysis
Diana Maria Popa, Simona-Vasilica Oprea, Adela Bâra
This paper investigates how generative-artificial intelligence AI is reshaping job requirements, skill compositions and sectoral dynamics across global labor markets. It examines t…
Preference learning in shades of gray: Interpretable and bias-aware reward modeling for human preferences
Simona-Vasilica Oprea, Adela Bâra
Learning human preferences in language models remains fundamentally challenging, as reward modeling relies on subtle, subjective comparisons or shades of gray rather than clear-cut…
Measuring the Fragility of Trust: Devising Credibility Index via Explanation Stability (CIES) for Business Decision Support Systems
Alin-Gabriel Vaduva, Simona-Vasilica Oprea, Adela Bara
Explainable Artificial Intelligence (XAI) methods (SHAP, LIME) are increasingly adopted to interpret models in high-stakes businesses. However, the credibility of these explanation…