collaborators

7 papers

cs.CL2026

Encoded but Not Routed: Explaining the Table-Chart Gap in Scientific Claim Verification

Sunisth Kumar, Xanh Ho, Tim Schopf +3

Multimodal LLMs are increasingly used to assist scientific peer review, where a core requirement is verifying whether claims in a paper are supported by its evidence. Prior work ha…

cs.IR2026

Aspect-Aware Content-Based Recommendations for Mathematical Research Papers

Ankit Satpute, André Greiner-Petter, Noah Gießing +4

Content-based research paper recommendation (CbRPR) has seen advances in computer science and biomedicine, but remains unexplored for mathematics, where paper relatedness is more c…

cs.CL2026

SciClaimEval: Cross-modal Claim Verification in Scientific Papers

Xanh Ho, Yun-Ang Wu, Sunisth Kumar +4

We present SciClaimEval, a new scientific dataset for the claim verification task. Unlike existing resources, SciClaimEval features authentic claims, including refuted ones, direct…

cs.CL2026

Overview of PAN 2026: Voight-Kampff Generative AI Detection, Text Watermarking, Multi-Author Writing Style Analysis, Generative Plagiarism Detection, and Reasoning Trajectory Detection

Janek Bevendorff, Maik Fröbe, André Greiner-Petter +9

The goal of the PAN workshop is to advance computational stylometry and text forensics via objective and reproducible evaluation. In 2026, we run the following five tasks: (1) Voig…

cs.CL2025

Overview of the Plagiarism Detection Task at PAN 2025

André Greiner-Petter, Maik Fröbe, Jan Philip Wahle +4

The generative plagiarism detection task at PAN 2025 aims at identifying automatically generated textual plagiarism in scientific articles and aligning them with their respective s…

cs.CY2025

MAGPIE: Multi-Task Media-Bias Analysis Generalization for Pre-Trained Identification of Expressions

Tomáš Horych, Martin Wessel, Jan Philip Wahle +6

Media bias detection poses a complex, multifaceted problem traditionally tackled using single-task models and small in-domain datasets, consequently lacking generalizability. To ad…