While LLMs have substantially improved the quality of generated texts, they still tend to make errors in their outputs, which can be subtle and harder to find than for older approaches. This needs to be reflected in the evaluation, where standard metrics or simple scores may not capture errors easily. While human evaluation should produce better ...

Ondřej Dušek is an Assistant Professor at Charles University in Prague, working on natural language generation and human-computer dialogue. His research focuses on generative language models including large language models, mostly applied to the data-to-text and dialogue response generation tasks. He is specifically interested in evaluating the ...

The growing adoption of foundation models calls for a paradigm shift from Data Science to Model Science. Unlike data-centric approaches, Model Science places the trained model at the core of analysis, aiming to interact, verify, explain, and control its behavior across diverse operational contexts. This paper introduces a conceptual framework for ...

Przemysław Biecek, is a Director of the Centre for Credible AI. His research focuses on Model Science, with emphasis on model interpretability, controllability, and verifiability. He has authored or co-authored more than 200 scientific publications, including books and monographs, and has led numerous research and applied projects on ...

Deep learning continues to achieve impressive breakthroughs across disciplines but relies on increasingly large neural network models that are trained on massive data sets. Their development inflicts costs that are only affordable by a few labs and prevent global participation in the creation of related technologies. In this talk, we will ask the ...

Rebekka leads the Relational Machine Learning Group at the Helmholtz Center CISPA since 2021. Their research on sparse deep learning is supported by an ERC starting grant since December 2023 and Apple Research since August 2025. From 2019-2021, she was a PostDoc at the Biostatistics Department of the Harvard T.H. Chan School of Public Health ...

The recent revolution in generative AI has been driven by the rapid growth in training and inference compute for Foundation Models (FMs). This scaling, however, brings unintended consequences, including high energy demand and associated carbon emissions. My research sets out to reverse this trend by targeting a fundamental inefficiency in dominant ...

I am an assistant professor in Natural Language Processing at the University of Edinburgh and an affiliated lecturer at the University of Cambridge. Over the past year, I was a visiting professor at NVIDIA. My research focuses primarily on efficient and modular architectures for foundation models, especially with respect to adaptive memory and ...

In this presentation, I will describe the dynamically evolving landscape of formal verification of mathematical theorems. Currently, with the help of generative AI methods, the field of mathematics is undergoing a major transformation, in which mathematical proofs are becoming reusable commodities, and challenges such as project scalability, ...

Dr Bartosz Naskręcki is a graduate of Adam Mickiewicz University and specializes in number theory and algebraic geometry. After obtaining his doctorate in 2014, he completed several years of research internships in Bayreuth, Bavaria, Bristol, UK, and at the Dioscuri TDA Center in Warsaw. For over four years, he has been collaborating with Prof. ...

Foundation models have delivered a step change over prior task‑specific machine learning approaches by learning reusable pretrained representations, enabling rapid adaptation across tasks and strong performance even with limited labeled data in new domains. However, significant limitations remain that hinder their practical usability and ...

Artur Dubrawski is the Alumni Research Professor of Computer Science at Carnegie Mellon University and Director of the Auton Lab, a research group focused on building mathematically founded AI that works in the real world. With over 35 years of experience across academia and industry, Professor Dubrawski has led projects in healthcare, defense, ...

We introduce Meta Agents Research Environments (ARE), a research platform for scalable creation of environments, integration of synthetic or real applications, and execution of agentic orchestrations. ARE provides simple abstractions to build complex and diverse environments, each with their own rules, tools, content, and verifiers, helping to ...

Pierre Ménard is a Research Scientist at Meta, where he works on large language model agents. His research focuses on reinforcement learning, with particular interests in multi-agent systems, unsupervised learning, and reinforcement learning from human feedback. Before joining Meta, he was a postdoctoral researcher, most recently at ENS Lyon. He ...

Predicting tumor drug response is a fundamental challenge in precision oncology, complicated by cellular heterogeneity, where minor resistant sub-populations drive clinical relapse. While single-cell transcriptomics offers a high-resolution view of these mixtures, a significant domain gap remains: most drug-response labels originate from bulk ...

Dr.Marina Esteban-Medina is a postdoctoral researcher at the ETH Zürich AI Center and ETH Zürich’s Department of Biosystems Science and Engineering (Switzerland). She works at the intersection of AI and translational biomedicine, developing machine-learning and statistical models to understand tumor heterogeneity and treatment response, with a ...

We propose a new framework for generative modeling based on a discrete-time stochastic control formulation of measure transport. Adapting classic results from control theory, we formulate our problem as a linear program whose dual variables correspond to the optimal value function of the control problem, which directly encode the optimal control ...

Gergely Neu is an ICREA research professor at Universitat Pompeu Fabra, Barcelona, Spain. He has previously worked with the SequeL team of INRIA Lille, France and the RLAI group at the University of Alberta, Edmonton, Canada. He obtained his PhD degree in 2013 from the Budapest University of Technology and Economics, where his advisors were ...