1 citations · 2 across the 16 of their papers we have counts for
6 papers · 1 filter
Optimal Watermark Generation under Type I and Type II Errors
Hengzhi He, Shirong Xu, Alexander Nemecek +3
Watermarking has recently emerged as a crucial tool for protecting the intellectual property of generative models and for distinguishing AI-generated content from human-generated d…
Exploring Membership Inference Vulnerabilities in Clinical Large Language Models
Alexander Nemecek, Zebin Yun, Zahra Rahmani +4
As large language models (LLMs) become progressively more embedded in clinical decision-support, documentation, and patient-information systems, ensuring their privacy and trustwor…
ZKPROV: A Zero-Knowledge Approach to Dataset Provenance for Large Language Models
Mina Namazi, Alexander Nemecek, Erman Ayday
As large language models (LLMs) are used in sensitive fields, accurately verifying their computational provenance without disclosing their training datasets poses a significant cha…
Watermarking Without Standards Is Not AI Governance
Alexander Nemecek, Yuzhou Jiang, Erman Ayday
Watermarking has emerged as a leading technical proposal for attributing generative AI content and is increasingly cited in global governance frameworks. This position paper argues…
The Feasibility of Topic-Based Watermarking on Academic Peer Reviews
Alexander Nemecek, Yuzhou Jiang, Erman Ayday
Large language models (LLMs) are increasingly integrated into academic workflows, with many conferences and journals permitting their use for tasks such as language refinement and…
Scalar subleading soft theorems from an infinite tower of charges
Matías Briceño, Hernán A. González, Alfredo Pérez
We investigate conservation laws in the absence of gauge invariance by analyzing interacting scalar theories with a massless scalar field. We construct an infinite tower of charges…