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Thomas Seidl

13 papers hereh-index 346 citations14 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author7
  • last author5

Across the 12 of 13 papers where every author was matched, so the position is known.

fields
  • cs.CV9
  • cs.CL4
same name
  • Thomas Seidl — 4 papers, h 1
  • Thomas Seidl — 3 papers, h 2
  • Thomas Seidl — 2 papers, h 3
  • Thomas Seidl — 1 paper, h 3
  • Thomas Seidl — 1 paper, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20242026
collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2026

Beyond Input Understanding: Diagnosing Multilingual Mathematical Reasoning with Directed Acyclic Trace Graphs

Jiaqiao Zhang, Zhoujun Li, Raoyuan Zhao +5

Large reasoning models (LRMs) achieve strong mathematical reasoning performance in English, but remain much less reliable in many low- and medium-resource languages. This gap is of…

cs.CL2026

Unveiling the "Fairness Seesaw": Discovering and Mitigating Gender and Race Bias in Vision-Language Models

Jian Lan, Udo Schlegel, Tanveer Hannan +3

Although Vision-Language Models (VLMs) have achieved remarkable success, the knowledge mechanisms underlying their social biases remain a black box, where fairness- and ethics-rela…

cs.CL2025

PRSM: A Measure to Evaluate CLIP's Robustness Against Paraphrases

Udo Schlegel, Franziska Weeber, Jian Lan +1

Contrastive Language-Image Pre-training (CLIP) is a widely used multimodal model that aligns text and image representations through large-scale training. While it performs strongly…

cs.CL2025

Enhancing Robustness of Autoregressive Language Models against Orthographic Attacks via Pixel-based Approach

Han Yang, Jian Lan, Yihong Liu +2

Autoregressive language models are vulnerable to orthographic attacks, where input text is perturbed with characters from multilingual alphabets, leading to substantial performance…

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