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<!DOCTYPE html>
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<title>Wolfgang Stammer - Symbolic Concepts, Explanations and Interactions ... and stuff</title>
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<ul>
<li><a href="#about" class="active">About</a></li>
<li><a href="#publications">Publications</a></li>
<li><a href="#music">Music</a></li>
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<div class="intro">
<img src="images/wstammer3.jpeg" alt="Your Photo">
<div class="intro-text">
<h1>Wolfgang Stammer</h1>
<h2>Symbolic Concepts, Explanations and Interactions</h2>
<p>Postdoctoral Researcher</p>
<p>CV & ML Group, Max Planck Institute for Informatics</p>
<p>Associated Member, "Neuroexplicit Models" Research Training Group</p>
<p>Email: wolfgang [dot] stammer [at] mpi-inf [dot] mpg [dot] de</p>
</div>
</div>
<section id="about">
<p>Currently, I am a Postdoctoral Researcher in the Computer Vision and Machine Learning Group at the Max Planck Institute for Informatics, under the supervision of Prof. Bernt Schiele. Additionally, I am an associated member of the Research Training Group "Neuroexplicit Models" at Saarland University. I completed my PhD with excellence under the supervision of Prof. Kristian Kersting in the AI & ML lab at the TU Darmstadt (<a href="https://drive.google.com/file/d/1x-MCyEQ3XLYTUPYjw9203GIfJVjmGfax/view?usp=sharing">thesis pdf here</a>).</p>
<p>My research revolves around the question: "How can we create <span style="color: #759fE5;">performative</span> AI models that allow users to <span style="color:#a5d781;">understand</span> and <span style="color:#6d57d1;">interact</span> with their <span style="color:#DF9D9B;">internal representations</span>?"</p>
<p>I believe that for AI systems to explain their decisions effectively, they must communicate with human stakeholders using verifiable, concept-level statements. Importantly, this communication should not be one-sided; like human conversations, it should involve active discussion and interaction.</p>
<p>As part of this, I'm also interested in how cognitive systems—whether biological or artificial—can learn abstract concepts without strong supervision. How can they bind information to a specific representation? What kind of representation is it?</p>
<p><b>Events</b> I co-organised (Workshops, Tutorials, Symposiums):</p>
<ul>
<li>"Semantic, Symbolic and Interpretable Machine Learning" ELLIS Workshop at Elise Wrap Up Conference 2024</li>
<li>"Interactive Machine Learning" Workshop at AAAI 2022</li>
<li>"Explanations in Interactive Machine Learning" Tutorial at AAAI 2022</li>
<li>"Perspectives on Learning" Doctoral Symposium on Cognitive Science of <i>German Society for Cognitive Science</i>, 2022</li>
</ul>
<p><b>Invited Lectures</b>:</p>
<ul>
<li>"Concept-based Explanations" @ Karlsruhe Institute of Technology (KIT), 2025 as part of block lecture "Explainable Artificial Intelligence"</li>
</ul>
<p>Apart from research I am very passionate about writing and playing <b>music</b> (see below :)). </p>
</section>
<section id="publications">
<h2>Selected Publications</h2>
<p>Show me in <a href="https://scholar.google.com/citations?user=66-aU5AAAAAJ&hl" class="scholar-link">Google Scholar</a></p>
<p></p>
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<h3>What’s in the Bottle? A Survey and Roadmap of Concept Bottleneck Models</h3>
<p>Patrick Knab, David Steinmann, Christian Bartelt, Kristian Kersting, Bernt Schiele, Thomas Seidl, Udo Schlegel, <u>Wolfgang Stammer</u> </p>
<a href="https://www.researchgate.net/profile/Patrick-Knab-2/publication/401288530_What's_in_the_Bottle_A_Survey_and_Roadmap_of_Concept_Bottleneck_Models/links/69a148dbe8fd5476473bb092/Whats-in-the-Bottle-A-Survey-and-Roadmap-of-Concept-Bottleneck-Models.pdf" class="publication-link">[Research Gate]</a>
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<h3>Synthesizing Visual Concepts as Vision-Language Programs</h3>
<p>Antonia Wüst, <u>Wolfgang Stammer</u>, Hikaru Shindo, Lukas Helff, Devendra Singh Dhami, Kristian Kersting</p>
<a href="https://arxiv.org/pdf/2511.18964" class="publication-link">[CVPR 2026]</a>
<a href="https://github.com/ml-research/vision-language-programs" class="github-link">[GitHub]</a>
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<h3>ActivationReasoning: Logical Reasoning in Latent Activation Spaces</h3>
<p>Lukas Helff, Ruben Härle, <u>Wolfgang Stammer</u>, Felix Friedrich, Manuel Brack, Antonia Wüst, Hikaru Shindo, Patrick Schramowski, Kristian Kersting</p>
<a href="https://www.arxiv.org/pdf/2510.18184" class="publication-link">[ICLR 2026]</a>
<a href="https://github.com/ml-research/ActivationReasoning" class="github-link">[GitHub]</a>
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<img src="papers/helff2025slr.png" alt="Publication Image">
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<h3>SLR: An Automated Synthesis Framework for Scalable Logical Reasoning</h3>
<p>Lukas Helff, Ahmad Omar, Felix Friedrich, Antonia Wüst, Tim Woydt, Rupert Mitchell, Patrick Schramowski, <u>Wolfgang Stammer</u>, Kristian Kersting</p>
<a href="https://arxiv.org/pdf/2506.15787" class="publication-link">[Arxiv]</a>
<a href="https://github.com/ml-research/ScalableLogicalReasoning" class="github-link">[GitHub]</a>
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<img src="papers/steinmann2025ocb.png" alt="Publication Image">
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<h3>Object-Centric Concept Bottlenecks</h3>
<p>David Steinmann, <u>Wolfgang Stammer</u>, Antonia Wüst, Kristian Kersting</p>
<a href="https://arxiv.org/pdf/2505.24492" class="publication-link">[NeurIPS 2025]</a>
<a href="https://github.com/DavSte13/Object-Centric-Concept-Bottlenecks/" class="github-link">[GitHub]</a>
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<h3>Neural Concept Verifier: Scaling Prover-Verifier Games via Concept Encodings</h3>
<p>Berkant Turan, Suhrab Asadulla, David Steinmann, <u>Wolfgang Stammer</u>, Sebastian Pokutta</p>
<a href="https://arxiv.org/pdf/2507.07532" class="publication-link">[Actionable Interpretability Workshop @ICML 2025]</a>
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<h3>Where is the Truth? The Risk of Getting Confounded in a Continual World</h3>
<p>Florian Peter Busch, Roshni Kamath, Rupert Mitchell, <u>Wolfgang Stammer</u>, Kristian Kersting, Martin Mundt</p>
<a href="https://arxiv.org/pdf/2402.06434" class="publication-link">[ICML 2025 (spotlight)]</a>
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<h3>Bongard in Wonderland: Visual Puzzles that Still Make AI Go Mad?</h3>
<p>Antonia Wüst, Tim Tobiasch, Lukas Helff, Inga Ibs, <u>Wolfgang Stammer</u>, Devendra S. Dhami, Constantin A. Rothkopf, Kristian Kersting</p>
<a href="https://arxiv.org/pdf/2410.19546" class="publication-link">[ICML 2025]</a>
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<img src="papers/Thesis.png" alt="Publication Image">
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<h3>The Value of Symbolic Concepts for AI Explanations and Interactions (Dissertation)</h3>
<p><u>Wolfgang Stammer</u></p>
<a href="https://drive.google.com/file/d/1x-MCyEQ3XLYTUPYjw9203GIfJVjmGfax/view?usp=sharing" class="publication-link">[PhD Thesis (compressed file)]</a>
<a href="https://tuprints.ulb.tu-darmstadt.de/29712/" class="publication-link">[Technical University Darmstadt]</a>
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<h3>Human-in-the-loop or AI-in-the-loop? Automate or Collaborate?</h3>
<p>Sriraam Natarajan, Saurabh Mathur, Sahil Sidheekh, <u>Wolfgang Stammer</u>, Kristian Kersting</p>
<a href="https://arxiv.org/pdf/2412.14232" class="publication-link">[AAAI 2025]</a>
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<img src="papers/stammer2024neurips.png" alt="Publication Image">
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<h3>Neural Concept Binder</h3>
<p><u>Wolfgang Stammer</u>, Antonia Wüst, David Steinmann, Kristian Kersting</p>
<a href="https://arxiv.org/pdf/2406.09949" class="publication-link">[NeurIPS 2024]</a>
<a href="https://github.com/ml-research/NeuralConceptBinder" class="github-link">[GitHub]</a>
</div>
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<img src="papers/delfosse2024interpretable.png" alt="Publication Image">
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<h3>Interpretable Concept Bottlenecks to Align Reinforcement Learning Agents</h3>
<p>Quentin Delfosse, Sebastian Sztwiertnia, Mark Rothermel, <u>Wolfgang Stammer</u>, Kristian Kersting</p>
<a href="https://arxiv.org/pdf/2401.05821" class="publication-link">[NeurIPS 2024]</a>
<a href="https://github.com/k4ntz/SCoBots" class="github-link">[GitHub]</a>
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<h3>Pix2code: Learning to compose neural visual concepts as programs</h3>
<p>Antonia Wüst, <u>Wolfgang Stammer</u>, Quentin Delfosse, Devendra Singh Dhami, Kristian Kersting</p>
<a href="https://arxiv.org/pdf/2402.08280" class="publication-link">[UAI 2024] (oral)</a>
<a href="https://github.com/ml-research/pix2code" class="github-link">[GitHub]</a>
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<img src="papers/stammer2024tmlr.png" alt="Publication Image">
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<h3>Learning by Self-Explaining</h3>
<p><u>Wolfgang Stammer</u>, Felix Friedrich, David Steinmann, Hikaru Shindo, Kristian Kersting</p>
<a href="https://arxiv.org/pdf/2309.08395" class="publication-link">[TMLR 2024]</a>
<a href="https://github.com/ml-research/learning-by-self-explaining" class="github-link">[GitHub]</a>
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<h3>V-LoL: A Diagnostic Dataset for Visual Logical Learning</h3>
<p>Lukas Helff, <u>Wolfgang Stammer</u>, Hikaru Shindo, Devendra Singh Dhami, Kristian Kersting</p>
<a href="https://arxiv.org/pdf/2306.07743" class="publication-link">[DMLR 2024]</a>
<a href="https://sites.google.com/view/v-lol" class="project-page">[Project Page]</a>
<a href="https://huggingface.co/datasets/AIML-TUDA/v-lol-trains" class="huggingface-link">[HuggingFace]</a>
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<h3>Learning to Intervene on Concept Bottlenecks</h3>
<p>David Steinmann, <u>Wolfgang Stammer</u>, Felix Friedrich, Kristian Kersting</p>
<a href="https://arxiv.org/pdf/2308.13453" class="publication-link">[ICML 2024]</a>
<a href="https://github.com/ml-research/CB2M" class="github-link">[GitHub]</a>
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<h3>Leveraging explanations in interactive machine learning: An overview</h3>
<p>Stefano Teso, Öznur Alkan, <u>Wolfgang Stammer</u>, Elizabeth Daly</p>
<a href="https://www.frontiersin.org/articles/10.3389/frai.2023.1066049/full" class="publication-link">[Frontiers in AI 2023]</a>
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<h3>A typology for exploring the mitigation of shortcut behaviour</h3>
<p>Felix Friedrich, <u>Wolfgang Stammer</u>, Patrick Schramowski, Kristian Kersting</p>
<a href="https://www.nature.com/articles/s42256-023-00612-w" class="publication-link">[Nature Machine Intelligence 2023]</a>
<a href="https://github.com/ml-research/A-Typology-for-Exploring-the-Mitigation-of-Shortcut-Behavior" class="github-link">[GitHub]</a>
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<h3>Boosting Object Representation Learning via Motion and Object Continuity</h3>
<p>Quentin Delfosse, <u>Wolfgang Stammer</u>, Thomas Rothenbacher, Dwarak Vittal, Kristian Kersting</p>
<a href="https://arxiv.org/pdf/2211.09771" class="publication-link">[ECML-PKDD 2023]</a>
<a href="https://github.com/k4ntz/MOC" class="github-link">[GitHub]</a>
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<img src="papers/friedrich2022RiT.png" alt="Publication Image">
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<h3>Revision Transformers: Instructing Language Models to Change Their Values</h3>
<p>Felix Friedrich, <u>Wolfgang Stammer</u>, Patrick Schramowski, Kristian Kersting</p>
<a href="https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA230341" class="publication-link">[ECAI 2023]</a>
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<h3>Interactive Disentanglement: Learning Concepts by Interacting with their Prototype Representations</h3>
<p><u>Wolfgang Stammer</u>, Marius Memmel, Patrick Schramowski, Kristian Kersting</p>
<a href="https://openaccess.thecvf.com/content/CVPR2022/papers/Stammer_Interactive_Disentanglement_Learning_Concepts_by_Interacting_With_Their_Prototype_Representations_CVPR_2022_paper.pdf" class="publication-link">[CVPR 2022]</a>
<a href="https://github.com/ml-research/XIConceptLearning" class="github-link">[GitHub]</a>
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<h3>Neural-Probabilistic Answer Set Programming</h3>
<p>Arseny Skryagin, <u>Wolfgang Stammer</u>, Daniel Ochs, Devendra Singh Dhami, Kristian Kersting</p>
<a href="https://proceedings.kr.org/2022/48/kr2022-0048-skryagin-et-al.pdf" class="publication-link">[KR 2022]</a>
<a href="https://github.com/ml-research/SLASH" class="github-link">[GitHub]</a>
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<h3>Right for better reasons: Training differentiable models by constraining their influence functions</h3>
<p>Xiaoting Shao, Arseny Skryagin, <u>Wolfgang Stammer</u>, Patrick Schramowski, Kristian Kersting</p>
<a href="https://ojs.aaai.org/index.php/AAAI/article/view/17148/16955" class="publication-link">[AAAI 2021]</a>
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<img src="papers/stammer2021cvpr.png" alt="Publication Image">
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<h3>Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting With Their Explanations</h3>
<p><u>Wolfgang Stammer</u>, Patrick Schramowski, Kristian Kersting</p>
<a href="http://openaccess.thecvf.com/content/CVPR2021/papers/Stammer_Right_for_the_Right_Concept_Revising_Neuro-Symbolic_Concepts_by_Interacting_CVPR_2021_paper.pdf" class="publication-link">[CVPR 2021]</a>
<a href="https://github.com/ml-research/NeSyXIL" class="github-link">[GitHub]</a>
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<h3>Making deep neural networks right for the right scientific reasons by interacting with their explanations</h3>
<p>Patrick Schramowski, <u>Wolfgang Stammer</u>, Stefano Teso, Anna Brugger, Franziska Herbert, Xiaoting Shao, Hans-Georg Luigs, Anne-Katrin Mahlein, Kristian Kersting</p>
<a href="https://www.nature.com/articles/s42256-020-0212-3" class="publication-link">[Nature Machine Intelligence 2020]</a>
<a href="https://github.com/ml-research/XIL" class="github-link">[GitHub]</a>
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<section id="music">
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<h2>Music</h2>
<img src="images/Spiderwebs.jpeg" alt="Spiderwebs & Foam">
<p>In addition to my work in AI and machine learning, I am also passionate about music. I play and write lyrics and music in the semi-professional band <a href="https://spiderwebsandfoam.com" target="_blank">Spiderwebs & Foam</a>. Our band blends various genres, from Rock, Jazz, Electronic, Folk. We have performed at several venues and continue to create and share our music with the world.</p>
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<p>© 2026 Wolfgang Stammer. All rights reserved.</p>
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