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Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

In this meeting of the Research Training Group "Neuroexplicit Models of Language, Vision, and Action", we will discuss current research and trends in combining neural and human-interpretable models. These model architectures have complementary strengths with respect to generalization, robustness, and interpretability. The overall goal of the RTG is to develop novel neuroexplicit models that accurately solve tasks in natural language processing, computer vision, and action-decision making, and to investigate the theoretical and practical principles of designing effective neuroexplicit models.

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

Inductive programming (IP) research – also called inductive program synthesis – is concerned with learning computer programs from data, typically input/output examples. It incorporates many areas of computer science, especially machine learning, automated reasoning, program verification, programming language theory, and software engineering. IP also has strong relations to research outside computer science, notably in cognitive science, where it can help build models of human inductive learning and intelligent tutoring systems for programming education. In industry, IP supports tools for end-user programming such as the Microsoft Excel plug-in FlashFill.

Génie logiciel,    Réseaux de neurones et intelligence artificielle, apprentissage automatique

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

This Dagstuhl Seminar addresses research gaps in the continual assessment of AI systems amid post-deployment changes in requirements, user-specific objectives, deployment environments, and the AI systems themselves.

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

Recent years have seen the emergence of a number of research groups that aim to advance quantum computing and to develop a skilled quantum software workforce. A particular challenge that must be overcome for safe quantum computing at scale is the creation of robust, expressive, efficient, and tractable formal verification tools. Since there is currently no event dedicated to this pursuit, the first goal of this Dagstuhl Seminar is to bring together a dedicated community of researchers in a single venue. This will give participants an opportunity to identify and discuss the most pressing challenges and promising directions for quantum programming and verification.

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

The study of clustering problems dates back several decades, and has led to the adaptation of versatile and powerful algorithm design techniques. In recent years, there has been a surge of activities in the study of clustering problems from different computational vantage points, leading to various new techniques for the development of approximation algorithms. These include new algorithm prototypes that are applicable to sublinear models, and geometric tools to deal with the challenges of high dimensionality. Each of these strands of research focuses on some challenge faced in modern applications involving massive and rapidly evolving data sets, and attempts to design clustering algorithms that overcome this challenge. This had led to a series of recent results on dynamic, online, streaming and massively parallel clustering, as well as on designing clustering algorithms whose outputs are fair, robust, and differentially private. The seminar aims to bring together researchers working on clustering problems from these divergent perspectives.

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

Réseaux de neurones et intelligence artificielle, apprentissage automatique,    Interface homme-machine, expérience utilisateur, la réalité virtuelle

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

AI is transforming computer security in ways that have significant potential to disrupt the effectiveness of current defensive standards. The vulnerabilities of the AI systems themselves, along with their potential use as offensive tools, introduce a new dimension to the threat modeling of computer systems. As AI models increasingly power autonomous agents, this attack surface continues to expand, giving rise to novel threats that impact both society and industry. At the same time, AI models and autonomous agents could also serve as transformative tools for developing advanced defensive techniques. The rapid pace of AI development creates significant uncertainty about what will be required to defend against AI-based attacks and how to devise autonomous cyber defenses. By reducing reliance on manual intervention and integrating with existing defense mechanisms, AI defensive systems may offer new ways to counter attacks and mitigate threats. This Dagstuhl Perspectives Workshop aims to develop a systematic understanding of how AI agents can be used in both offensive and defensive security.

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

Human-Computer Interaction (HCI) is undergoing a profound epistemological shift. For decades, the field has developed a rich repertoire of research methods informed by its interdisciplinary origins. These methods have enabled HCI researchers to explore phenomena across a wide range of domains—from desktop work environments to everyday life—with approaches that span controlled experiments, ethnographic fieldwork, and design interventions. However, the increasing integration of artificial intelligence (AI), and particularly generative AI (genAI), into both interactive systems and research workflows is disrupting the foundational assumptions behind these methods. This Dagstuhl Seminar invites participants to examine how HCI research methods must adapt in light of these developments—and what new opportunities and risks this transformation entails.

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

music information retrieval; audio signal processing; deep learning; open source; user interaction and interfaces

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

Energy; Critical Infrastructure; Explainable AI; Machine Learning

Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH