Publications

Refactoring with LLMs: Bridging Human Expertise and Machine Understanding

Yonnel Chen Kuang Piao

Jean Carlors Paul

Leuson Da Silva

Arghavan Moradi Dakhel

Mohammad Hamdaqa

Foutse Khomh

2025-10-03

ArXiv (prépublication)

doi.org

arxiv.org

Capacity Planning in Stable Matching

Federico Bobbio

Margarida Carvalho

Andrea Lodi

Ignacio Rios

Alfredo Torrico

We introduce the problem of jointly increasing school capacities and finding a student-optimal assignment in the expanded market. Due to the… (voir plus) impossibility of efficiently solving the problem with classical methods, we generalize existent mathematical programming formulations of stability constraints to our setting, most of which result in integer quadratically-constrained programs. In addition, we propose a novel mixed-integer linear programming formulation that is exponentially large on the problem size. We show that its stability constraints can be separated by exploiting the objective function, leading to an effective cutting-plane algorithm. We conclude the theoretical analysis of the problem by discussing some mechanism properties. On the computational side, we evaluate the performance of our approaches in a detailed study, and we find that our cutting-plane method outperforms our generalization of existing mixed-integer approaches. We also propose two heuristics that are effective for large instances of the problem. Finally, we use the Chilean school choice system data to demonstrate the impact of capacity planning under stability conditions. Our results show that each additional seat can benefit multiple students and that we can effectively target the assignment of previously unassigned students or improve the assignment of several students through improvement chains. These insights empower the decision-maker in tuning the matching algorithm to provide a fair application-oriented solution.

2025-10-02

Operations Research (publié)

doi.org

arxiv.org

Improving GUI Grounding with Explicit Position-to-Coordinate Mapping

Suyuchen Wang

Tianyu Zhang

Ahmed Masry

Christopher Pal

Spandana Gella

Bang Liu

Perouz Taslakian

2025-10-02

ArXiv (prépublication)

doi.org

arxiv.org

Asymmetric Proximal Policy Optimization: mini-critics boost LLM reasoning

Jiashun Liu

Johan Samir Obando Ceron

Han Lu

Yancheng He

Weixun Wang

Wenbo Su

Bo Zheng

Pablo Samuel Castro

Aaron Courville

Ling Pan

Most recent RL for LLMs (RL4LLM) methods avoid explicit critics, replacing them with average advantage baselines. This shift is largely prag… (voir plus)matic: conventional value functions are computationally expensive to train at LLM scale and often fail under sparse rewards and long reasoning horizons. We revisit this bottleneck from an architectural perspective and introduce Asymmetric Proximal Policy Optimization (AsyPPO), a simple and scalable framework that restores the critics role while remaining efficient in large-model settings. AsyPPO employs a set of lightweight mini-critics, each trained on disjoint prompt shards. This design encourages diversity while preserving calibration, reducing value-estimation bias. Beyond robust estimation, AsyPPO leverages inter-critic uncertainty to refine the policy update: (i) masking advantages in states where critics agree and gradients add little learning signal, and (ii) filtering high-divergence states from entropy regularization, suppressing spurious exploration. After training on open-source data with only 5,000 samples, AsyPPO consistently improves learning stability and performance across multiple benchmarks over strong baselines, such as GRPO, achieving performance gains of more than six percent on Qwen3-4b-Base and about three percent on Qwen3-8b-Base and Qwen3-14b-Base over classic PPO, without additional tricks. These results highlight the importance of architectural innovations for scalable, efficient algorithms.

2025-10-01

ArXiv (prépublication)

doi.org

arxiv.org

Genetic contribution to asthma informs acute chest syndrome pathophysiology and risk stratification

Sara El Aouhel

Vanessa Bellegarde

Stennio Da

Silva Faria

Tristan St-Laurent

Estelle Lecluze

Anne-Laure Pham Hung d’Alexandry d’Orengiani

F. Galactéros

Pablo Bartolucci

Marc-André Legault

Guillaume Lettre

Thomas Pincez

2025-10-01

medRxiv (prépublication)

doi.org

GRACE: A Language Model Framework for Explainable Inverse Reinforcement Learning

Silvia Sapora

R Devon Hjelm

Alexander T Toshev

Omar Attia

Bogdan Mazoure

Inverse Reinforcement Learning aims to recover reward models from expert demonstrations, but traditional methods yield"black-box"models that… (voir plus) are difficult to interpret and debug. In this work, we introduce GRACE (Generating Rewards As CodE), a method for using Large Language Models within an evolutionary search to reverse-engineer an interpretable, code-based reward function directly from expert trajectories. The resulting reward function is executable code that can be inspected and verified. We empirically validate GRACE on the BabyAI and AndroidWorld benchmarks, where it efficiently learns highly accurate rewards, even in complex, multi-task settings. Further, we demonstrate that the resulting reward leads to strong policies, compared to both competitive Imitation Learning and online RL approaches with ground-truth rewards. Finally, we show that GRACE is able to build complex reward APIs in multi-task setups.

2025-10-01

ArXiv (prépublication)

doi.org

arxiv.org

Task Mapping Strategies for Electric Power System Simulations on Heterogeneous Clusters

Julie Durette

Gunes Karabulut Kurt

Antoine Lesage-Landry

In this work, we propose improved task mapping strategies for real-time electric power system simulations on heterogeneous computing cluster… (voir plus)s, considering both heterogeneous communication links and processing capacities, with a focus on bottleneck objectives. We approach the problem through two complementary models: the bottleneck quadratic semi-assignment problem (BQSAP), which optimizes task configuration for a fixed number of computing nodes while minimizing communication and computation costs; and the variable-size bin packing problem with quadratic communication constraints (Q-VSBPP), which minimizes the required number of computing nodes, valuable for resource provisioning scenarios. We extend the PuLP library to solve approximately both problems, explicitly including communication costs and processing constraints, and formalize the nomenclature and definitions for bottleneck objectives in graph partitioning. This formalization fills a gap in the existing literature and provides a framework for the rigorous analysis and application of task mapping techniques to real-time electric power system simulation. Finally, we provide a quantitative study and benchmark the extended PuLP library with the SCOTCH partitioning library in the context of real-time electromagnetic transient (EMT) simulation task mapping.

2025-10-01

2025 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) (publié)

doi.org

Attention-Based Multi-Agent RL for Multi-Machine Tending Using Mobile Robots

Abdalwhab Bakheet Mohamed Abdalwhab

Giovanni Beltrame

Samira Ebrahimi Kahou

David St-Onge

Robotics can help address the growing worker shortage challenge of the manufacturing industry. As such, machine tending is a task collaborat… (voir plus)ive robots can tackle that can also greatly boost productivity. Nevertheless, existing robotics systems deployed in that sector rely on a fixed single-arm setup, whereas mobile robots can provide more flexibility and scalability. We introduce a multi-agent multi-machine-tending learning framework using mobile robots based on multi-agent reinforcement learning (MARL) techniques, with the design of a suitable observation and reward. Moreover, we integrate an attention-based encoding mechanism into the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm to boost its performance for machine-tending scenarios. Our model (AB-MAPPO) outperforms MAPPO in this new challenging scenario in terms of task success, safety, and resource utilization. Furthermore, we provided an extensive ablation study to support our design decisions.

2025-09-30

AI (publié)

doi.org

Combining virtual reality and hypnosis to alleviate chronic pain in elderly with hand arthritis: protocol for a randomised phase II clinical trial

Valentyn Fournier

Marie-Fania Simard

Sai Yan Yuen

Joséphine Guiné

Floriane Rousseaux

Julie Lebeau

Karim Jerbi

Philippe Richebé

Mathieu Landry

Pierre Rainville

David Ogez

Chronic pain is a common health condition that significantly impacts the quality of life of those affected, affecting one in five people in … (voir plus)Canada. The prevalence of this condition tends to increase with age, making it a major health issue given the ageing population. However, its management remains inadequate and requires significant mobilisation of healthcare professionals as well as the development of multiple therapeutic solutions. Among these, non-pharmacological interventions such as hypnosis and virtual reality have proven effective. Nevertheless, while the existing literature seems promising, it presents methodological limitations. Therefore, this study aims to assess the effectiveness of an intervention combining virtual reality and hypnosis in an ageing population suffering from a widespread chronic pain condition, that is, hand arthritis. This study will be a single-centre randomised clinical trial. Participants will be randomly assigned to one of two conditions: one receiving an intervention combining virtual reality and hypnosis, and the other receiving only virtual reality. The effectiveness of the intervention on current perceived pain before and after the intervention (primary outcome) will be evaluated. Secondary outcomes will include anxiety and depressive symptoms, quality of life, relaxation and fatigue. Exploratory analyses will also be conducted to contribute to the emerging literature by examining physiological variables such as heart rate variability, respiratory rate and electrodermal activity during the intervention, and their relationship with primary and secondary outcomes. The project was approved by the Research Ethical Committee of the Hospital Maisonneuve-Rosemont (Project no 2024-3539). Participants will be asked to provide written consent for their participation. Results from this study will be shared through peer-reviewed publications, as well as oral and poster presentations at scientific events. The protocol for this study was preregistered on Open Science Framework and raw anonymised data will be available on this platform ( https://osf.io/vbh72/?view_only=1d17c5708f894faab6669d85e1fde75d ). NCT06833905 .

2025-09-30

BMJ Open (publié)

doi.org

Current landscape of clinical genetics knowledge and attitudes among Non-Geneticist Physicians - the McGill genetics education survey (McGES).

Sarah Abdullah-Maklan

Yannis Trakadis

2025-09-30

Journal of Community Genetics (publié)

doi.org

Equivariant Geometric Scattering Networks via Vector Diffusion Wavelets

David R. Johnson

Rishabh Anand