Publications

Prompting fairness: Learning prompts for debiasing large language models

Authors: Lemnaru Camelia, Rad Cristian Andrei

Large language models are prone to internalize social biases due to the characteristics of the data used for their self-supervised training scheme. Considering their recent emergence and wide availability to the general public, it is mandatory to identify and alleviate these biases to avoid perpetuating stereotypes towards underrepresented groups. We present a novel prompt-tuning method for reducing biases in encoder models such as BERT or RoBERTa. Unlike other methods, we only train a small set of additional reusable token embeddings that can be concatenated to any input sequence to reduce bias in the outputs. We particularize this method to gender bias by providing a set of templates used for training the prompts. Evaluations on two benchmarks show that our method is on par with the state of the art while having a limited impact on language modeling ability

A Hybrid Machine Learning–Genetic Algorithm for Optimizing Surface-Mount Technology Planning

Authors: Groza Adrian Petru

We tackle the problem of improving the Surface- Mount Technology (SMT) process planning in an automotive manufacturing setting. Current simulations show low accu- racy across production lines as the existing approach relies on predefined setups rather than adapting to product-specific configurations. We propose a hybrid framework that couples machine learning with a genetic algorithm to generate product- specific plans. Our solution involves three tasks: (i) assigning boards to lines, (ii) allocating components to Pick-and-Place (PnP) machines, and (iii) balancing workloads across machines. Our hybrid pipeline embeds supervised learning in a genetic optimizer. A multi-class classifier selects feasible PnP head con- figurations per Bill of Materials (BOM) part number (precision = 0.73). A genetic algorithm assigns components to compatible feeder tables/machines, while a regression model estimates table cycle times (R² = 0.88). The fitness jointly optimizes Components Placed per Hour (CPH) and Line Balancing (LB) under process constraints. Different mutation methods are explored, revealing that mutation based on balancing the workload by leveling the number of placements on the tables with minimum and maximum cycle time results in an LB of 0.83, with a CPH of 0.37 and an average delta cycle time of -3.27% across 105-part numbers

Reducing Hallucinations in Medical AI: A Knowledge Graph-Augmented Retrieval System for Evidence-Based Age-Related Macular Degeneration Information

Authors: Alexandru Lecu, Groza Adrian Petru

Large language models (LLMs) have significantly advanced natural language generation but frequently produce unverified outputs, compromising their reliability in critical medical applications. We present a framework that combines structured biomedical knowledge with LLMs through retrieval-augmented generation to address this challenge. Our system automatically extracts causal relationships from 5 000 age-related macular degeneration (AMD) abstracts, building a knowledge graph with over 43 200 validated relations. Using vector-based retrieval, the framework generates contextually relevant and verifiable responses with direct clinical evidence links. We evaluated our approach across eight language models, including open-source models from 1B to 70B parameters (LLama, Mistral, Qwen, SmolLM) and GPT-5-mini, on 3 000 queries with varying question types and reasoning complexity. Smaller models (3B parameters) showed substantial improvements: SmolLM3-3B reached 95.6% accuracy on singlehop true/false questions (from 78.2% baseline). The medium-scale model Mistral-7B demonstrated the largest gains on complex multi-hop reasoning, improving from 45% to 76% accuracy on multiple-choice questions. Larger models (70B parameters) showed minimal improvement due to already high baseline performance (97-98% accuracy). Our results demonstrate that RAG-enhanced knowledge graphs enable resource-efficient smaller models to achieve performance levels approaching or matching larger models, reducing hallucinations while maintaining computational efficiency for clinical deployment [PDF](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11298209)

Fine-Grained Complexity of Ontology Mediated Queries

Authors: Feier Cristina

MCP-Orchestrated Multi-Agent System for Automated Disinformation Detection

Authors: Groza Adrian Petru, Alexandru Lecu

The large spread of disinformation across digital platforms creates significant challenges to information integrity. This paper presents a multi-agent system that uses relation extraction to detect disinformation in news articles, focusing on titles and short text snippets. The proposed Agentic AI system combines four agents: (i) a machine learning agent (logistic regression), (ii) a Wikipedia knowledge check agent (which relies on named entity recognition), (iii) a coherence detection agent (using LLM prompt engineering), and (iv) a web-scraped data analyzer that extracts relational triplets for fact checking. The system is orchestrated via the Model Context Protocol (MCP), offering shared context and live learning across components. Results demonstrate that the multi-agent ensemble achieves 95.3% accuracy with an F1 score of 0.964, significantly outperforming individual agents and traditional approaches. The weighted aggregation method, mathematically derived from individual agent misclassification rates, proves superior to algorithmic threshold optimization. The modular architecture makes the system easily scalable, while also maintaining details of the decision processes.

A Comparative Survey of Social Bias in Text and Image Generation: Gaps, Directions and Compliance with the EU AI Act

Authors: Rad Cristian Andrei, Lemnaru Camelia

Generative artificial intelligence models, including large language models and image generation models, are increasingly deployed in socially impactful domains. However, these models often exhibit social biases that can amplify stereotypes and produce harmful, discriminatory outputs. In this paper, we present a modality-comparative survey of social bias in text and image generation, structured around four components: benchmarks, bias identification, measurement, and mitigation. We systematically analyze methodological parallels and divergences across the two modalities, highlighting emerging research trends and identifying gaps. Finally, we map current image generation research efforts to the EU AI Act’s technical requirements, offering insights into how the community can advance towards more fair, safe, and trustworthy systems.

Prompts and Prayers: the Rise of GPTheology

Authors: Groza Adrian Petru

Increasingly artificial intelligence (AI) has been cast in “god-like” roles (to name a few: film industry – Matrix, The Creator, Mission Impossible, Foundation, Dune etc.; literature – Children of Time, Permutation City, Neuromancer, I Have no Mouth and I Must Scream, Alphaville etc.). This trend has accelerated with the advent of sophisticated Large Language Models such as ChatGPT. For this phenomenon, where AI is perceived as divine, we use the term GPTheology, where ChatGPT and other AI models are treated as potential oracles of a semi-divine nature. This paper explores the emergence of GPTheology as a form of techno-religion, examining how narratives around AI echo traditional religious constructs. We draw on community narratives from online forums – Reddit – and recent projects – AI-powered Mazu Statue in Malaysia (Lu, 2025); “ShamAIn” Project in Korea (He-rim, 2025); AI Jesus in a Swiss Church (Kennedy, 2024). These examples show striking similarities to technological notions of the Singularity and the development of Artificial General Intelligence (AGI). Additionally, we analyse how daily interactions with AI are acquiring ritualistic associations and how AI-centric ideologies clash with or are integrated into established religions. This study uses a dataset of Reddit posts discussing AI to identify recurring themes of salvation, prophecy, and demonization surrounding AI. Our findings suggest that new belief systems are developing around AI, and this carries both philosophical and sociotechnical implications. Our paper critically analyses the benefits and dangers, as well as the social, political and ethical challenges of this development. This transdisciplinary inquiry highlights how AI and religion are increasingly intertwined, prompting necessary questions about humanity’s relationship with its creations and the future of belief.

Deep Clustering for Blood Cell Classification and Quantification

Authors: Groza Adrian Petru

Accurate classification of blood cells plays a key role in improving automated blood analysis for both medical and veterinary applications. This work presents a two-stage deep clustering method for classifying blood cells from high-dimensional signal data. In the first stage, red blood cells (RBCs) and platelets (PLTs) are separated using a combination of an improved autoencoder and the IDEC algorithm. The second stage further classifies RBC subtypes, pure RBCs, reticulocytes, and clumped RBCs, through a variational deep embedding (VaDE) approach. Due to the lack of detailed cell-level labels, soft classification probabilities are generated from sample-level data to approximate the true distributions. The aim is to contribute to the development of low-cost, automated blood analysis systems suitable for veterinary and biomedical use. Initial results indicate this method shows promise in effectively distinguishing different blood cell populations, even with limited supervision.

Emerging policies for the regulation of AI

Authors: Groza Adrian Petru, Lemnaru Camelia

The report analyzes emerging policies for the regulation of artificial intelligence (AI) and their interaction with existing regulations in related fields. The report has two parts. The first part examines the European Commission’s regulatory framework for artificial intelligence, its integration into emerging policies for the development of ethical AI, and its interaction with regulations in other sectors. The second part presents a possible institutional architecture for the regulation of AI in Romania. This vision involves a competent national authority for monitoring and regulating ethical AI, as well as a decentralized model in which conformity assessment bodies (e.g. audit centers, whether private or public) have responsibilities for monitoring, verification, and certification in different subdomains and technologies of artificial intelligence.