Invited Talk: Adil Zouitine - Scaling Tiny Vision-Language-Action Models to Real-Time Edge Robotics Room: Amphithéâtre Schwartz - 1R1 Title: **Scaling Tiny Vision-Language-Action Models to Real-Time Edge Robotics** **Bio**: Adil Zouitine is a founding research scientist at UMA, where he works on general-purpose mobile and humanoid robots capable of learning in real time. He holds a PhD in robust reinforcement learning, supervised by Emmanuel Rachelson. His research focuses on robustness to disturbances, sample efficiency, generalization, and adversarial reinforcement learning. After completing his PhD, Adil joined Hugging Face, where he helped build LeRobot, an open-source library for robot learning. He is also an active contributor to several open-source projects, including River, the online machine learning counterpart to scikit-learn.

Invited Talk: Alexandre Allauzen - Towards Frugal Deep Learning: Alternative to backprop, Data Sketching, and Causal Attention Room: Amphithéâtre Schwartz - 1R1 Title: **Towards Frugal Deep Learning: Alternative to backprop, Data Sketching, and Causal Attention** **Abstract:** This talk presents recent research conducted by the MILES team, within the framework of the PEPR SHARP project, toward the development of frugal deep learning. As contemporary AI systems continue to grow in scale, computational cost, and energy consumption, there is a need for learning paradigms that are more efficient and sustainable. Our work explores this challenge from three complementary perspectives: (i) learning algorithms that can approximate gradient-based optimization while maintaining strong learning capabilities; (ii) on the way to data efficiency, we explore dynamical sketching to summarize and organize data during the learning process; (iii) trading complexity for expressivity in the design of attention mecanism for causal decoding. **Bio:** Since November 2019, Alexandre Allauzen has been Professor at ESPCI Paris (École Supérieure de Physique et de Chimie Industrielles de la Ville de Paris). He is affiliated with LAMSADE (Laboratoire d’Analyse et de Modélisation de Systèmes pour l’Aide à la Décision) at Université Paris-Dauphine and leads the MILES (Machine Intelligence and Learning Systems) research team. His work focuses on frugal deep learning and its applications to speech processing and natural language processing. More recently, his research has expanded to machine learning for the sciences, with a particular emphasis on physics, in collaboration with the Institut Langevin (Smart-Waves program). Since September 2025, he has also served as Vice-President for Academic Affairs and Training at Université Paris Sciences et Lettres (PSL), where he contributes to the institution’s academic strategy and educational development.

Clovis Varangot-Reille Room: Amphithéâtre Schwartz - 1R3 Clovis Varangot-Reille, Wikit & Laboratoire Hubert Curien Abstract (TBA): Présentation générale du routing, avec un focus sur un routeur low-resource

Léopold Maytié Room: Amphithéâtre Schwartz - 1R3 Abstract: TBA

Laure Vieu Room: Amphithéâtre Schwartz - 1R3 Abstract: TBA

Mathieu Serrurier Room: Amphithéâtre Schwartz - 1R3 Abstract: TBA

Invited Talk: Ronan Sicre and Moncef Garouani Room: Amphithéâtre Schwartz - 1R1 Title: **TBA** **Abstract**; TBA **Bio**: Moncef Garouani is an Associate Professor of Computer Science at Université Toulouse Capitole and a researcher at IRIT. His research interests lie at the intersection of Machine Learning, Explainable Artificial Intelligence (XAI), multimodal learning, and Responsible AI. His work focuses on developing transparent, trustworthy, and human-centered AI systems, with particular emphasis on explainability, automated machine learning, multimodal data analysis, and the responsible design, governance, and deployment of AI. Ronan Sicre is junior professor at University of Toulouse and IRIT since 2025, after being assistant professor for 8 years at Ecole Centrale Méditerranée and LIS. He add prior postdoc positions at INRIA Rennes, University of Caen, University of Amsterdam and obtain his PhD from the University of Bordeaux. He works in the field of deep learning, computer vision and explainable AI.

Invited Talk: Meriem Belloucif - Low-Resource Preference Adaptation for LLMs Room: Amphithéâtre Schwartz - 1R1 Title: **Low-Resource Preference Adaptation for LLMs** **Abstract:** Adapting large language models to user-specific preferences is often constrained by the cost of human annotation, making preference optimisation impractical in low-resource settings where preferences cannot be reliably labelled by LLMs themselves, e.g., due to cultural, subjective, or personalised contexts. I will present ways for investigating how language models encode preference information in their intermediate representations, finding that activations from chosen and rejected responses form distinct clusters across layers, even in pretrained models. **Bio:** Meriem Beloucif is an Assistant Professor in Computational Linguistics at Uppsala University. Her research focuses on large language models for low-resource languages, including neural machine translation, lexical semantics, multilingual resource development, and the systematic evaluation of language models. Before, she worked on comparative QA and Information Retrieval at Hamburg University and spent a year at Copenhagen University as a Postdoc. She has a PhD in Computer Science from Hong Kong University of Science and Technology.

Invited Talk: Antonin Poché - Language models explainability Room: Amphithéâtre Schwartz - 1R1 Title: **Language models explainability** **Summary**: We will examine the specificity of language in explainability for both classification and generation. Study how explainability methods are adapted to text and what the state of the art currently is. Finally, through a tutorial, we will apply the methods seen in theory, notably with a bias detection use case. **Bio**: Antonin is a 2nd-year PhD Student in the Explainability of Language Models, between the IRT Saint Exupéry and the IRIT. He has been working on explainability for the last 5 years and is part of the developing teams of the Xplique and Interpreto explainability libraries.

Invited Talk: Didier Schwab Room: Amphithéâtre Schwartz - 1R1 Title: TBA **Bio:** Professeur des universités (CNU 27) -- Titulaire de la chaire AugmentIA pour l'humain augmenté par Intelligence Artificielle (Recherche, Innovation, Enseignement) -- Responsable de l'équipe GETALP (Groupe d’Étude en Traduction Automatique/Traitement Automatisé des Langues et de la Parole) du Laboratoire d'Informatique de Grenoble (~50 membres --permanent·e·s, doctorant·e·s, postdoctorant·e·s, ingénieur·e·s).

Invited Talk: Lihu Chen - Knowledge Boundary Awareness in Large Language Models

• Lihu Chen

Room: Amphithéâtre Schwartz - 1R1 Title: **Knowledge Boundary Awareness in Large Language Models** **Abstract**: Large language models (LLMs) possess remarkable knowledge and reasoning capabilities, yet their capabilities are bounded. A fundamental challenge for trustworthy and efficient AI is enabling models to identify the limits of their own knowledge before producing an answer. In this talk, I will present our recent work on knowledge boundary awareness, a generation-free framework for estimating whether an LLM can answer a query. I will then discuss how knowledge boundary awareness helps build more efficient AI systems by reducing inference cost while maintaining the original performance. **Bio:** Lihu Chen is a Research Associate at Imperial College London. He received his Ph.D. from Télécom Paris (Institut Polytechnique de Paris) and previously held a postdoctoral position at Inria Saclay. His research focuses on natural language processing and large language models, with interests in trustworthy and efficient AI, information extraction, and biomedical NLP. He develops open-source models and tools to improve the reliability and efficiency of AI systems.