Publications

LogicXGNN: Grounded Logical Rules for Explaining Graph Neural Networks

Chuqin Geng

Ziyu Zhao

Zhaoyue Wang

Haolin Ye

Yuhe Jiang

Xujie Si

Existing rule-based explanations for Graph Neural Networks (GNNs) provide global interpretability but often optimize and assess fidelity in … (see more)an intermediate, uninterpretable concept space, overlooking the grounding quality of the final subgraph explanations for end users. This gap yields explanations that may appear faithful yet be unreliable in practice. To this end, we propose LogicXGNN, a post hoc framework that constructs logical rules over reliable predicates explicitly designed to capture the GNN's message-passing structure, thereby ensuring effective grounding. We further introduce data-grounded fidelity (

2025-12-31

International Conference on Learning Representations (Accept (Poster))

Long-Horizon Model-Based Offline Reinforcement Learning Without Conservatism

2025-12-31

International Conference on Machine Learning (Accept (regular))

Do machine learning methods make better predictions than conventional ones in pharmacoepidemiology? A systematic review, meta-analysis, and network meta-analysis.

Ana Paula Bruno Pena-Gralle

Mireille E. Schnitzer

Sofia-Nada Boureguaa

Félix Morin

Marc-André Legault

Caroline Sirois

Alice Dragomir

Lucie Blais

2025-12-31

Artif. Intell. Medicine (published)

MANSION: Multi-floor lANguage-to-3D Scene generatIOn for loNg-horizon tasks

Lirong Che

Shuo Wen

Shan Huang

Chuang Wang

Yuzhe Yang

Gregory Dudek

Xueqian Wang

Jian Su

Real-world robotic tasks are long-horizon and often span multiple floors, requiring complex spatial reasoning. Existing embodied benchmarks,… (see more) however, are largely confined to single-floor homes, failing to evaluate agents on realistic, building-scale tasks. We introduce MANSION, a language-driven framework for generating building-scale, multi-floor 3D environments for long-horizon tasks. Using this framework, we release MansionWorld, a large-scale dataset featuring over 1,000 diverse, non-residential buildings. These environments support cross-floor skills and long-horizon task generation on reusable building layouts. Experiments show that current methods degrade sharply on our multi-floor tasks, highlighting both the challenge and the value of this setting for advancing embodied AI.

2025-12-31

IEEE/CVF Conference on Computer Vision and Pattern Recognition (Accept (Poster))

Mapping of Subjective Accounts into Interpreted Clusters (MOSAIC): Topic Modelling and LLM applied to Stroboscopic Phenomenology

Romy Beauté

David J. Schwartzman

Guillaume Dumas

Jennifer Crook

Fiona Macpherson

Adam B. Barrett

Anil K. Seth

Abstract Stroboscopic light stimulation (SLS) on closed eyes typically induces simple visual hallucinations, characterized by vivid, geometr… (see more)ic, and colourful patterns. A dataset of 898 sentences, extracted from 407 open subjective reports, was recently compiled as part of the Dreamachine programme (https://dreamachine.world/) (Collective Act, 2022), an immersive multisensory experience that combines SLS and spatial sound in a collective setting. Although open reports extend the range of reportable phenomenology, their analysis presents significant challenges, particularly in systematically identifying patterns. To address this challenge, we implemented a data-driven approach leveraging large language models and topic modelling to uncover and interpret latent experiential topics directly from the Dreamachine’s text-based reports. Our analysis confirmed the presence of simple visual hallucinations typically documented in scientific studies of SLS, while also revealing experiences of altered states of consciousness and complex hallucinations. Building on these findings, our computational approach expands the systematic study of subjective experience by enabling data-driven analyses of open-ended phenomenological reports, capturing experiences not readily identified through standard questionnaires. By revealing rich and multifaceted aspects of experiences, our study broadens our understanding of stroboscopically induced phenomena while highlighting the potential of natural language processing and large language models in the field of computational phenomenology. More generally, this approach provides a practically applicable methodology for uncovering subtle hidden patterns of subjective experience across diverse research domains. Open-source implementation and an interactive web application are provided to facilitate application of this methodology.

2025-12-31

Neuroscience of Consciousness (published)

MedRiskEval: Medical Risk Evaluation Benchmark of Language Models, On the Importance of User Perspectives in Healthcare Settings

Jean-Philippe Corbeil

Minseon Kim

Maxime Griot

Sheela Agarwal

Alessandro Sordoni

Francois Beaulieu

Paul Vozila

Jean-Philippe Corbeil, Minseon Kim, Maxime Griot, Sheela Agarwal, Alessandro Sordoni, Francois Beaulieu, Paul Vozila. Proceedings of the 19t… (see more)h Conference of the European Chapter of the Association for Computational Linguistics (Volume 5: Industry Track). 2026.

2025-12-31

Conference of the European Chapter of the Association for Computational Linguistics (published)

Model Collapse Is Not a Bug but a Feature in Machine Unlearning for LLMs

Yan Scholten

Sophie Xhonneux

Leo Schwinn

Stephan Günnemann

Current unlearning methods for LLMs optimize on the private information they seek to remove by incorporating it into their fine-tuning data.… (see more) We argue this not only risks reinforcing exposure to sensitive data, it also fundamentally contradicts the principle of minimizing its use. As a remedy, we propose a novel unlearning method—Partial Model Collapse (PMC), which does not require unlearning targets in the unlearning objective. Our approach is inspired by recent observations that training generative models on their own generations leads to distribution collapse, effectively removing information from model outputs. Our central insight is that model collapse can be leveraged for machine unlearning by deliberately triggering it for data we aim to remove. We theoretically analyze that our approach converges to the desired outcome, i.e. the model unlearns the data targeted for removal. We empirically demonstrate that PMC overcomes three key limitations of existing unlearning methods that explicitly optimize on unlearning targets, and more effectively removes private information from model outputs while preserving general model utility. Overall, our contributions represent an important step toward more comprehensive unlearning that aligns with real-world privacy constraints.

2025-12-31

International Conference on Learning Representations (Accept (Poster))

Alireza Dehghanpour Farashah

Multilingual Amnesia: On the Transferability of Unlearning in Multilingual LLMs

As multilingual large language models become more widely used, ensuring their safety and fairness across diverse linguistic contexts present… (see more)s unique challenges. While existing research on machine unlearning has primarily focused on monolingual settings, typically English, multilingual environments introduce additional complexities due to cross-lingual knowledge transfer and biases embedded in both pretraining and fine-tuning data. In this work, we study multilingual unlearning using the Aya-Expanse 8B model under two settings: (1) data unlearning and (2) concept unlearning. We extend benchmarks for factual knowledge and stereotypes to ten languages through translation: English, French, Arabic, Japanese, Russian, Farsi, Korean, Hindi, Hebrew, and Indonesian. These languages span five language families and a wide range of resource levels. Our experiments show that unlearning in high-resource languages is generally more stable, with asymmetric transfer effects observed between typologically related languages. Furthermore, our analysis of linguistic distances indicates that syntactic similarity is the strongest predictor of cross-lingual unlearning behavior.

2025-12-31

Conference of the European Chapter of the Association for Computational Linguistics (published)

Neurosymbolic Large Neighbourhood Search

Arnaud Delage-Reid

Gilles Pesant

Amal Zouaq

Recent advances in ai have spurred interest in NeSy architectures that integrate neural and symbolic methods. In particular, combining a Con… (see more)straint Programming (cp) model with a language model for constrained sequence generation tasks allows the neural component to capture domain knowledge while cp enforces structural constraints. In this paper we propose combining cp with a Masked Language Model (mlm) to perform Large Neighbourhood Search (lns). Unlike conventional left-to-right Large Language Models, mlm s can complete sequences with gaps in arbitrary positions, making them well-suited for this task. Meanwhile, lns provides a cp-based iterative framework to explore constrained subspaces whenever searching the whole space would be intractable. We evaluate NeSylns on tasks in constrained text generation and molecule discovery. Our experiments show that it can quickly generate many high-quality sentences and molecules, even for highly-constrained tasks.

2025-12-31

Polytechnique Montréal (accepted)

Nonlinear Observer Design for Visual-Inertial Odometry

Mouaad Boughellaba

Abdelhamid Tayebi

James Richard Forbes

Soulaimane Berkane

This paper addresses the problem of Visual-Inertial Odometry (VIO) for rigid body systems evolving in three-dimensional space. We introduce … (see more)a novel matrix Lie group structure, denoted SE_{3+n}(3), that unifies the pose, gravity, linear velocity, and landmark positions within a consistent geometric framework tailored to the VIO problem. Building upon this formulation, we design an almost globally asymptotically stable nonlinear geometric observer that tightly integrates data from an Inertial Measurement Unit (IMU) and visual sensors. Unlike conventional Extended Kalman Filter (EKF)-based estimators that rely on local linearization and thus ensure only local convergence, the proposed observer achieves almost global stability through the decoupling of the rotational and translational dynamics. A globally exponentially stable Riccati-based translational observer along with an almost global input-to-state stable attitude observer are designed such that the overall cascaded observer enjoys almost global asymptotic stability. This cascaded architecture guarantees robust and consistent estimation of the extended state, including orientation, position, velocity, gravity, and landmark positions, up to the VIO unobservable directions (i.e., a global translation and rotation about gravity). The effectiveness of the proposed scheme is demonstrated through numerical simulations as well as experimental validation on the EuRoC MAV dataset, highlighting its robustness and suitability for real-world VIO applications.

2025-12-31

arXiv (preprint)

OBELiX: a curated dataset of crystal structures and experimentally measured ionic conductivities for lithium solid-state electrolytes

Félix Therrien

Jamal Abou Haibeh

Divya Sharma

Rhiannon Hendley

Leah Wairimu Mungai

Sun Sun

Alain Tchagang

Jiang Su

Samuel Huberman

Yoshua Bengio

Hongyu Guo

Alex Hernández-García

Homin Shin

OBELiX is a database of 599 synthesized solid electrolyte materials and their experimentally measured room temperature ionic conductivities … (see more)gathered from literature and curated by domain experts.

2025-12-31

Digital Discovery (published)