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.
We are very grateful for the invitation!
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Causal foundation models promise fast, flexible treatment-effect estimation from observational data, but can their uncertainty be trusted?
The post of PhD Student Valentyn Melnychukexamines this question in light of the new class of foundation models for tabular and causal inference, called prior-data fitted networks (PFNs). He found that the answer is not always positive, but that PFN-based uncertainty can behave much more like the uncertainty from classical causal frequentist estimators when calibrated appropriately.
Large language models (LLMs) process vast amounts of information to produce impressive outputs, such as summarizing texts, answering questions, and classifying sentiment. However, their operation often feels like a black box. 🤔 How do they make predictions? Can we identify which input information is relevant to the model's final decision?
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.
We are happy to announce that relAI will be represented in Seoul, South Korea, from July 6 to 11 at the Forty-Third International Conference on Machine Learning (ICML2026)!. ICML is widely recognized as one of the top three most influential conferences in machine learning and artificial intelligence research.
📖 About relAI Publications at ICML
relAI contributes fourteen publications to the conference, including nine in the main track.
relAI PhD student Emre Kavakwill present a Spotlight paper (ranked in the top 2.2%).
Five relAI papers will be presented at ICML Workshops, with contributions from three relAI MSc students.
🤝Meet relAI Students
If you are attending ICML we encourage you to engage in discussions about relAI research with the following relAI students present at the conference:
