Author

O. Kotevska

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#natural language process... Aug 2025

Majority Bit-Aware Watermarking For Large Language Models

A novel message encoding paradigm is proposed called \textit{majority bit-aware encoding}, which relaxes the watermark signal strength from the green list size, which allows for a strong watermark signal to be preserved in generated texts even when using a large green list.

Jiahao Xu, Rui Hu, O. Kotevska et al. · arXiv.org · 3 citations

#software testing Preprint Oct 2026

Cross-Facility LLM Pre-training on HPC: Elastic Aggregation, Data Leasing, and Queue-Aware Placement

Academic compute is fragmented: allocations are granted per facility, and facilities differ in accelerators and software stacks, schedule jobs independently, and share neither a network nor a filesystem. We present a system that pools such allocations to pre-train a single language model across three supercomputers on...

Zarè Palanciyan, Thomas van Osch, Douwe van der Wal et al. · 0 citations

Preprint Sep 2026

Privacy Foundations for Multi-Institutional Scientific Artificial Intelligence

Scientific artificial intelligence (AI), spanning foundation models (FMs) to federated data-analysis pipelines, is becoming shared infrastructure across national laboratories, universities, hospitals, and industrial partners. This collaboration creates privacy risks whose natural unit is often an institution's particip...

O. Kotevska, Sumit Kumar Jha, A. Bellet et al. · 1 citation

Preprint Aug 2026

Enforcing LLM Safety through DMD-based Classification of Prompt-Response Embedding Dynamics

Large Language Models (LLMs) are increasingly deployed in high-stakes applications, yet their tendency to generate toxic, harmful, or policy-violating content poses significant risks. Detecting these unsafe outputs efficiently in a black-box manner remains an open challenge. In this paper, we extend a recently proposed...

Mohamed Akrout, O. Kotevska, Dan Wilson · 0 citations

Preprint Aug 2026

Consilience: Conformally Calibrated Communication Control for Hidden-Profile Multi-Agent Reasoning

Multi-agent LLM systems can improve reasoning by pooling diverse perspectives, but their effectiveness depends on coordinating communication, particularly in hidden-profile settings where each agent holds only part of the evidence required for a correct decision. Existing protocols, including fixed schedules, round-rob...

Abhijith Babu, Ramneet Kaur, Vishal Pramanik et al. · 0 citations

Preprint Aug 2026

Extending the Horizon of Early Diagnosis: Lung Cancer Prediction with Vision Transformers

Although performance remains below clinical deployment thresholds, the results support further development of ViT-based triage systems to flag high-risk patients for earlier evaluation and demonstrate the potential of ViTs for early lung cancer risk prediction from routine chest X-rays.

O. Kotevska, Ian Goethert, Michael McGee et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Forgetting Only What Matters: Layer-Selective Unlearning toward Robust LLMs

This work proposes Forgetting Only What Matters via Unlearning Layers (FOM-UL), a layer-level unlearning framework that selects transformer layers using a forget-to-retain significance score and provides an empirical path toward quantization-resilient unlearning.

Ravi Ranjan, O. Kotevska, Agoritsa Polyzou · 3 citations