12 papers
Too much evidence, too little time: From text to actionable recommendations through multi-objective evidence reasoning
Adela Bara, Simona-Vasilica Oprea
Evidence-based clinical decision making requires specialists to identify, evaluate and synthesize relevant scientific literature. However, PubMed searches for complex clinical case…
AttackPathGNN: Cross-function vulnerability detection in smart contracts using state interference graphs and conjunction pooling
Gabriela Dobrita, Simona-Vasilica Oprea, Adela Bara
Existing learning-based detectors for Solidity smart-contracts reduce vulnerability detection to syntactic pattern matching within single functions, yet many of the most consequent…
Code2UML: Agentic LLMs with context engineering for scalable software visualization
Alin-Gabriel Văduva, Anca-Ioana Andreescu, Simona-Vasilica Oprea +1
Large Language Model (LLM)-based code analysis tools are adopted to automate software documentation tasks. However, the scalability of these approaches to real codebases, where Int…
CHAINTRIX: A multi-pipeline LLM-augmented framework for automated smart-contract security auditing
Gabriela Dobrita, Simona-Vasilica Oprea, Adela Bara
Smart-contract exploits have caused billions of USD in cumulative losses, yet audits remain expensive and slow. Automated tools have emerged to close this gap, but each class has a…
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…
Learning after COVID-19 and the ICT career aspirations: Are students entering the AI era with weaker skills?
Diana Maria Popa, Simona-Vasilica Oprea, Adela Bâra
This paper examines whether students are entering the generative AI era with sufficiently strong educational foundations, focusing on the relationship between learning environments…
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…
AI-Augmented Bibliometric Framework: A Paradigm Shift with Agentic AI for Dynamic, Snippet-Based Research Analysis
Adela Bara, Simona-Vasilica Oprea
Our paper introduces a generative, multiagent AI framework designed to overcome the rigidity, limited flexibility and technical barriers of current bibliometric tools. The objectiv…