We are proud to announce that relAI MSc students Nikolai Röhrich and Isabell Hans have received the 🏆 Best Paper Award 🏆 at the German Conference on Pattern Recognition (GCPR) for their paper titled “Concept Guidance: Precise, Training-Free Latent Control for Text-to-Image Generation.”
The article introduces Concept Guidance (CoG), a method that improves control over specific visual concepts in text-to-image diffusion models without requiring retraining, external models, or prompt engineering. 🧐If you are interested in learning more about CoG, you can read the full publication here: https://arxiv.org/abs/2608.14172
On September 22, researchers from relAI, MDSI, and our industry partner SAP came together at the SAP Labs Munich Campus in Garching for the second edition of our Pitchtalks Session.
The event, chaired by Dr. Tobias Müller (SAP), opened with welcoming remarks from Sylvia Kortüm, Managing Director of MDSI, and Dr. Anja Woracek, the new relAI Coordinator. relAI and MDSI researchers then presented their work, followed by lively discussions and networking opportunities.
🌟Highlights from the scientific presentations included:
• Bhavatarini Kumaravel(MDSI) exploring graph-based deep reinforcement learning for structural design
• Annika Schneider(relAI) presenting research on uncertainty quantification in probabilistic models
• Sameer Ambekar(relAI & MDSI): discussing distribution shifts in vision-language models
🤝 The networking session provided an excellent opportunity for researchers and industry experts to exchange ideas, discuss current and emerging challenges in AI, and explore future collaborations.
A big thank you to SAP for hosting and organizing this inspiring event. We look forward to continuing to strengthen the connection between cutting-edge AI research and industry practice in future editions.
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🎉Congratulations!
We are very excited to announce that relAI Fellow Vincent Fortuin has been awarded a European Research Council (ERC) Starting Grant for his project "AutoBayes", with the title "Unlocking Reliable Small-data AI through Bayesian Deep Learning".
Most current deep learning models require large amounts of training data and tend to be overconfident in their predictions, undermining their reliability. Over the next five years, Vincent and his team will develop Bayesian deep learning methods to create AI models that are confident using limited data while also improving the quantification of the uncertainty in their predictions.
The ERC Starting Grant is one of the most competitive sources of funding available to early-career researchers; it enables them to pursue ambitious projects, to form their own teams and to gain independence at a crucial point in their careers. This year, the funding—amounting to €705 million in total—has been awarded to 421 early-career researchers throughout Europe.
relAI is proud to support the Philosophy of Machine Learning Conference (PhilML'26), which will take place in Munich from October 6 to 9. Tom Sterkenburg, a relAI Fellow, is one of the main organizers of this event.
The conference will address fundamental epistemological, ethical, and social questions related to machine learning through the lens of analytic philosophy. We welcome the following types of work:
1. Research that applies philosophical concepts and methods to gain insights into machine learning.
2. Research that critically reflects on the philosophical and ethical implications of machine learning.
To promote close and productive dialogue, PhilML brings together philosophers and machine learning researchers with a philosophical inclination, encouraging direct engagement with scientific and mathematical details.
In this work, the authors derive the neural network Gaussian process (NNGP) limits of graph transformers, demonstrating the structural advantages of attention mechanisms compared to standard graph convolutions.
As an expert on social biases in AI models, Valentin was asked to comment on the controversial AI chatbot of state Senator Scott Wiener, which targeted Supervisor Connie Chan, Wiener's opponent in the 2026 California congressional election. The chatbot was accused of producing racist and sexist responses of Hong Kong-born Chan, who is a non-native English speaker.
In the interview, Hofmann explained that chatbots like Wiener’s are trained on large-scale internet data, which often contains racist and offensive stereotypes related to accents and citizenship. After reviewing the chatbot’s responses, Hofmann suggested that the model likely inferred from biographical and other available information that Connie Chan speaks with an accent. He noted that, in a failed attempt at satire, the chatbot exploited this information.
Hofmann's research demonstrates that linguistic stereotypes, particularly bias related to dialect and accent, are difficult to eliminate from AI models because they are encoded in subtle ways, in contrast to more explicit forms of racial prejudice. He also commented on the difficulties of eliminating problematic behaviour in AI chatbots and on current approaches to addressing it.
Join us for a lively meetup featuring leading researchers from MDSI and relAI at the SAP Labs Munich Campus Auditorium, where we will connect, discuss, and explore new ideas with our industry partner SAP.
🗓️ September 22, 2026, 4:00 - 5:30 pm (doors open at 3:30 pm, talks start at 16:00)
📍 SAP Labs Munich Campus (MUE03), Friedrich-Ludwig-Bauer-Straße 5, 85748 Garching bei München, Auditorium (AE.76)
Bhavatarini Kumaravel(MDSI): A Graph-Based Deep Q-Learning Agent for Grammar-Guided Structural Form-Finding Bhavatarini's supervisor is the MDSI Core Member Prof. Pierluigi D'Acunto, TUM Chair for Structural Design, TUM School of Engineering and Design
The work investigates how graph-based deep reinforcement learning can be combined with a structural grammar in finding novel and materially efficient structural design solutions.
Annika Schneider(relAI): Decision-aligned Evaluation of Uncertainty Quantification Annika is a relAI PhD student at Helmholtz Munich and TUM, supervised by relAI Fellow Dr. Vincent Fortuin, TUM Associate Professorship of AI for Scientific Modelling
This work investigates the question of how probabilistic models should be evaluated to ensure they perform in downstream decisions.
Sameer Ambekar(relAI & MDSI): Adressing Distribution Shifts: The Shift from Static to Thinking-based Vision-Language Models Sameer is a relAI PhD student at Helmholtz Munich and TUM, supervised by relAI Fellow and MDSI Core Member Prof. Julia Schnabel, TUM Chair for Computational Imaging and AI in Medicine
Distribution shifts have remained a persistent challenge over the years, with solutions spanning from domain adaptation to test-time training and, most recently, post-training mechanisms for vision-language models. This is because models inevitably encounter unseen data at inference time, data they were never trained for.
This talk at SAP by Sameer will focus on addressing this problem, tracing it from convolutional architectures to modern vision-language models and reasoning models, and connecting these stages through a consistent pattern: models that allocate additional computation at test time, whether through adaptation or reasoning, generalize more reliably than those relying solely on fixed, pre-trained parameters. Sketching through these topics from his PhD research, the talk will provide an overview of addressing unseen data at test time through training, reasoning, and post-training mechanisms.
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From July 28 to July 31, 2026, relAI hosted the second relAI International Summer School at LMU. This event focused on promoting educational exchange aimed at developing reliable AI and exploring current trends in the field.
Last week, our industry partner, QuantCo, hosted a networking event with relAI at their new Munich office.
The relAI students had a wonderful evening filled with engaging conversations and delicious food. They had the opportunity to meet colleagues from QuantCo's AI, and engineering teams and learned about how the company develops scalable systems to transform messy, real-world data into impactful decisions.
The summer school took place in Bologna from June 9 to June 17, 2026, and was a fantastic opportunity for young PhD students and researchers to explore the exciting connections between imaging sciences and machine/deep learning.
In her poster, Inés introduced CHEM (Conformal Hallucination Estimation Metric), a method for detecting and quantifying hallucinations in image reconstruction models. By combining wavelet and shearlet representations with conformalized quantile regression, CHEM identifies realistic-looking artifacts that aren’t present in the ground truth in a way that doesn’t depend on distribution.
