activity
20242026
most citedCOMPL-AI Framework: A Technical Interpretation and LLM Benchmarking Suite for the EU Artificial Intelligence Act

5 citations · 6 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL2026

Beyond Benchmarks: MathArena as an Evaluation Platform for Mathematics with LLMs

Jasper Dekoninck, Nikola Jovanović, Tim Gehrunger +4

Large language models (LLMs) are becoming increasingly capable mathematical collaborators, but static benchmarks are no longer sufficient for evaluating progress: they are often na…

cs.CR2025

Towards Watermarking of Open-Source LLMs

Thibaud Gloaguen, Nikola Jovanović, Robin Staab +1

While watermarks for closed LLMs have matured and have been included in large-scale deployments, these methods are not applicable to open-source models, which allow users full cont…

cs.CR2025★ 1 cited

BaxBench: Can LLMs Generate Correct and Secure Backends?

Mark Vero, Niels Mündler, Victor Chibotaru +5

Automatic program generation has long been a fundamental challenge in computer science. Recent benchmarks have shown that large language models (LLMs) can effectively generate code…

cs.AI2025

MathConstruct: Challenging LLM Reasoning with Constructive Proofs

Mislav Balunović, Jasper Dekoninck, Nikola Jovanović +2

While Large Language Models (LLMs) demonstrate impressive performance in mathematics, existing math benchmarks come with significant limitations. Many focus on problems with fixed…

cs.CL2024★ 5 cited

COMPL-AI Framework: A Technical Interpretation and LLM Benchmarking Suite for the EU Artificial Intelligence Act

Philipp Guldimann, Alexander Spiridonov, Robin Staab +9

The EU's Artificial Intelligence Act (AI Act) is a significant step towards responsible AI development, but lacks clear technical interpretation, making it difficult to assess mode…

cs.LG2024

Ward: Provable RAG Dataset Inference via LLM Watermarks

Nikola Jovanović, Robin Staab, Maximilian Baader +1

RAG enables LLMs to easily incorporate external data, raising concerns for data owners regarding unauthorized usage of their content. The challenge of detecting such unauthorized u…

cs.CR2024

Discovering Spoofing Attempts on Language Model Watermarks

Thibaud Gloaguen, Nikola Jovanović, Robin Staab +1

LLM watermarks stand out as a promising way to attribute ownership of LLM-generated text. One threat to watermark credibility comes from spoofing attacks, where an unauthorized thi…