13 citations · 65 across the 39 of their papers we have counts for
41 papers
Speak for Me: Giving LLMs the Situational Awareness to Participate in a Meeting
Muneeb Khan, Frederic Kirstein, Terry Ruas +1
In online meeting delegation, LLM agents fail to recognize when to speak. With no structured way to track stances, coverage, and floor, they miss the moments where they should cont…
Is Visual Prompting All You Need? Studying VLM Spatial Reasoning under Progressive Visual Scaffolds
Lars Benedikt Kaesberg, Tianyu Yang, Florian Valentin Wunderlich +4
Vision-language models (VLMs) have advanced rapidly in multimodal reasoning, yet recent work shows that their failures often reflect an interaction between visual grounding and dow…
BabelSteering: Multilingual Safety Alignment via English Steering Vectors
Emma V. Stein, Dominik Meier, Terry Ruas +2
Large language models (LLMs) are deployed globally in high-stakes settings, yet most safety research and alignment efforts remain concentrated on English. Thus, users interacting w…
Can Agents Deceive? Evaluating Reasoning and Deception in ParliamentBench using a Social Deduction Game
Niklas Bauer, Lars Benedikt Kaesberg, Akiko Aizawa +3
As large language models (LLMs) are deployed as agents in high-stakes settings, such as medical and legal systems, understanding their deceptive capabilities is fundamental to safe…
Misinformation Propagation in Benign Multi-Agent Systems
Jonas Becker, Jan Philip Wahle, Terry Ruas +1
Multi-agent systems (MAS) are credited with solving complex problems, yet their agents routinely act on context that may be false. Prior work shows single LLMs are swayed by false…
Beyond a Single Direction: Chain-of-Thought Disrupts Simple Steering of Refusal
Kia-Jüng Yang, Dominik Meier, Jiachen Zhao +2
Large reasoning models (LRMs) generate chain-of-thought (CoT) traces before producing final outputs, introducing a dynamic internal state that may complicate control mechanisms suc…
Multi-Agent Reasoning Improves Compute Efficiency: Pareto-Optimal Test-Time Scaling
Florian Valentin Wunderlich, Lars Benedikt Kaesberg, Jan Philip Wahle +2
Advances in inference methods have enabled language models to improve their predictions without additional training. These methods often prioritize raw performance over cost-effect…
Who Watches the Watchmen? Humans Disagree With Translation Metrics on Unseen Domains
Finn Schmidt, Jan Philip Wahle, Terry Ruas +1
Automatic evaluation metrics are central to the development of machine translation systems, yet their robustness under domain shift remains unclear. Most metrics are developed on t…
Mind the Gap Between Spatial Reasoning and Acting! Step-by-Step Evaluation of Agents With Spatial-Gym
Lars Benedikt Kaesberg, Tianyu Yang, Niklas Bauer +3
Spatial reasoning is central to navigation and robotics, yet measuring model capabilities on these tasks remains difficult. Existing benchmarks evaluate models in a one-shot settin…
SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis (DimABSA)
Liang-Chih Yu, Jonas Becker, Shamsuddeen Hassan Muhammad +14
We present the SemEval-2026 shared task on Dimensional Aspect-Based Sentiment Analysis (DimABSA), which improves traditional ABSA by modeling sentiment along valence-arousal (VA) d…
Piecing Together Cross-Document Coreference Resolution Datasets: Systematic Dataset Analysis and Unification
Anastasia Zhukova, Terry Ruas, Jan Philip Wahle +1
Research in CDCR remains fragmented due to heterogeneous dataset formats, varying annotation standards, and the predominance of the CDCR definition as the event coreference resolut…
DimStance: Multilingual Datasets for Dimensional Stance Analysis
Jonas Becker, Liang-Chih Yu, Shamsuddeen Hassan Muhammad +14
Stance detection is an established task that classifies an author's attitude toward a specific target into categories such as Favor, Neutral, and Against. Beyond categorical stance…
DimABSA: Building Multilingual and Multidomain Datasets for Dimensional Aspect-Based Sentiment Analysis
Lung-Hao Lee, Liang-Chih Yu, Natalia Loukashevich +13
Aspect-Based Sentiment Analysis (ABSA) focuses on extracting sentiment at a fine-grained aspect level and has been widely applied across real-world domains. However, existing ABSA…
Big Tech-Funded AI Papers Have Higher Citation Impact, Greater Insularity, and Larger Recency Bias
Max Martin Gnewuch, Jan Philip Wahle, Terry Ruas +1
Over the past four decades, artificial intelligence (AI) research has flourished at the nexus of academia and industry. However, Big Tech companies have increasingly acquired the e…
ALDEN: Reinforcement Learning for Active Navigation and Evidence Gathering in Long Documents
Tianyu Yang, Terry Ruas, Yijun Tian +3
Vision-language models (VLMs) excel at interpreting text-rich images but struggle with long, visually complex documents that demand analysis and integration of information spread a…
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…
Re-FRAME the Meeting Summarization SCOPE: Fact-Based Summarization and Personalization via Questions
Frederic Kirstein, Sonu Kumar, Terry Ruas +1
Meeting summarization with large language models (LLMs) remains error-prone, often producing outputs with hallucinations, omissions, and irrelevancies. We present FRAME, a modular…
MALLM: Multi-Agent Large Language Models Framework
Jonas Becker, Lars Benedikt Kaesberg, Niklas Bauer +3
Multi-agent debate (MAD) has demonstrated the ability to augment collective intelligence by scaling test-time compute and leveraging expertise. Current frameworks for multi-agent d…
TrojanStego: Your Language Model Can Secretly Be A Steganographic Privacy Leaking Agent
Dominik Meier, Jan Philip Wahle, Paul Röttger +2
As large language models (LLMs) become integrated into sensitive workflows, concerns grow over their potential to leak confidential information. We propose TrojanStego, a novel thr…
SPaRC: A Spatial Pathfinding Reasoning Challenge
Lars Benedikt Kaesberg, Jan Philip Wahle, Terry Ruas +1
Existing reasoning datasets saturate and fail to test abstract, multi-step problems, especially pathfinding and complex rule constraint satisfaction. We introduce SPaRC (Spatial Pa…
SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection
Shamsuddeen Hassan Muhammad, Nedjma Ousidhoum, Idris Abdulmumin +18
We present our shared task on text-based emotion detection, covering more than 30 languages from seven distinct language families. These languages are predominantly low-resource an…
Efficient Domain-adaptive Continual Pretraining for the Process Industry in the German Language
Anastasia Zhukova, Christian E. Matt, Bela Gipp
Domain-adaptive continual pretraining (DAPT) is a state-of-the-art technique that further trains a language model (LM) on its pretraining task, e.g., masked language modeling (MLM)…
Stay Focused: Problem Drift in Multi-Agent Debate
Jonas Becker, Lars Benedikt Kaesberg, Andreas Stephan +3
Multi-agent debate - multiple instances of large language models discussing problems in turn-based interaction - has shown promise for solving knowledge and reasoning tasks. Howeve…
Voting or Consensus? Decision-Making in Multi-Agent Debate
Lars Benedikt Kaesberg, Jonas Becker, Jan Philip Wahle +2
Much of the success of multi-agent debates depends on carefully choosing the right parameters. The decision-making protocol stands out as it can highly impact final model answers,…