7 citations · 9 across the 5 of their papers we have counts for
6 papers
Safety Signals to Verify NetOps Agents with Action-Level Granularity
Tobias Labarta, Frederik Pahde, Novak Boškov +6
Agentic Network Operations (NetOps) are an emerging paradigm promising to enable workload-aware, self-adjustable, and reliable autonomous networks. While agents have proven their v…
From Attribution to Action: A Human-Centered Application of Activation Steering
Tobias Labarta, Maximilian Dreyer, Katharina Weitz +2
Explainable AI (XAI) methods reveal which features influence model predictions, yet provide limited means for practitioners to act on these explanations. Activation steering of com…
X-SYS: A Reference Architecture for Interactive Explanation Systems
Tobias Labarta, Nhi Hoang, Maximilian Dreyer +5
The explainable AI (XAI) research community has proposed numerous technical methods, yet deploying explainability as systems remains challenging: Interactive explanation systems re…
See What I Mean? CUE: A Cognitive Model of Understanding Explanations
Tobias Labarta, Nhi Hoang, Katharina Weitz +3
As machine learning systems increasingly inform critical decisions, the need for human-understandable explanations grows. Current evaluations of Explainable AI (XAI) often prioriti…
Mechanistic understanding and validation of large AI models with SemanticLens
Maximilian Dreyer, Jim Berend, Tobias Labarta +4
Unlike human-engineered systems such as aeroplanes, where each component's role and dependencies are well understood, the inner workings of AI models remain largely opaque, hinderi…
Study on the Helpfulness of Explainable Artificial Intelligence
Tobias Labarta, Elizaveta Kulicheva, Ronja Froelian +3
Explainable Artificial Intelligence (XAI) is essential for building advanced machine learning-powered applications, especially in critical domains such as medical diagnostics or au…