Portrait of Quentin Cappart

Quentin Cappart

Affiliate Member
Assistant Professor, Polytechnique Montréal, Department of Computer Engineering and Software Engineering

Biography

Quentin Cappart is an assistant professor in the Department of Computer and Software Engineering at Polytechnique Montréal. He leads the CORAIL research group, which he co-founded with Louis-Martin Rousseau. Cappart obtained a BSc in engineering (2012), a MSc in computer engineering (2014), a MSc in management (2018) and a PhD (2017) at the Université catholique de Louvain (Belgium).

After his PhD, he joined Polytechnique Montréal and the International Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT) as a postdoctoral fellow (2018–2020). During these two years, he was also a research intern at ElementAI. Cappart’s main research area is the integration of machine learning with search procedures for solving combinatorial problems.

Publications

WorkArena: How Capable are Web Agents at Solving Common Knowledge Work Tasks?
Massimo Caccia
Issam Hadj Laradji
Manuel Del Verme
Tom Marty
Léo Boisvert
Megh Thakkar
David Vazquez
Alexandre Lacoste
WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?
Massimo Caccia
Issam Hadj Laradji
Manuel Del Verme
Tom Marty
Léo Boisvert
Megh Thakkar
David Vazquez
Alexandre Lacoste
We study the use of large language model-based agents for interacting with software via web browsers. Unlike prior work, we focus on measuri… (see more)ng the agents' ability to perform tasks that span the typical daily work of knowledge workers utilizing enterprise software systems. To this end, we propose WorkArena, a remote-hosted benchmark of 29 tasks based on the widely-used ServiceNow platform. We also introduce BrowserGym, an environment for the design and evaluation of such agents, offering a rich set of actions as well as multimodal observations. Our empirical evaluation reveals that while current agents show promise on WorkArena, there remains a considerable gap towards achieving full task automation. Notably, our analysis uncovers a significant performance disparity between open and closed-source LLMs, highlighting a critical area for future exploration and development in the field.
WorkArena: How Capable are Web Agents at Solving Common Knowledge Work Tasks?
Massimo Caccia
Issam Hadj Laradji
Manuel Del Verme
Tom Marty
Léo Boisvert
Megh Thakkar
David Vazquez
Alexandre Lacoste
We study the use of large language model-based agents for interacting with software via web browsers. Unlike prior work, we focus on measuri… (see more)ng the agents' ability to perform tasks that span the typical daily work of knowledge workers utilizing enterprise software systems. To this end, we propose WorkArena, a remote-hosted benchmark of 29 tasks based on the widely-used ServiceNow platform. We also introduce BrowserGym, an environment for the design and evaluation of such agents, offering a rich set of actions as well as multimodal observations. Our empirical evaluation reveals that while current agents show promise on WorkArena, there remains a considerable gap towards achieving full task automation. Notably, our analysis uncovers a significant performance disparity between open and closed-source LLMs, highlighting a critical area for future exploration and development in the field.
Deep Learning for Data-Driven Districting-and-Routing
Arthur Ferraz
Thibaut Vidal
Towards a Generic Representation of Combinatorial Problems for Learning-Based Approaches
Léo Boisvert
H'elene Verhaeghe
Learning Lagrangian Multipliers for the Travelling Salesman Problem
Augustin Parjadis
Bistra N. Dilkina
Aaron M. Ferber
Louis-Martin Rousseau
Global Rewards in Multi-Agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems
Heiko Hoppe
Tobias Enders
Maximilian Schiffer
Decision Diagrams in Space!
Isaac Rudich
Manuel L'opez-Ib'anez
Michael Romer
Louis-Martin Rousseau
The Unsolved Challenges of LLMs as Generalist Web Agents: A Case Study
Rim Assouel
Tom Marty
Massimo Caccia
Issam Hadj Laradji
Sai Rajeswar
Hector Palacios
David Vazquez
Alexandre Lacoste
Dynamic Routing and Wavelength Assignment with Reinforcement Learning
Peyman Kafaei
Hamed Pouya
Louis-Martin Rousseau
With the rapid developments in communication systems, and considering their dynamic nature, all-optical networks are becoming increasingly c… (see more)omplex. This study proposes a novel method based on deep reinforcement learning for the routing and wavelength assignment problem in all-optical wavelength-decision-multiplexing networks. We consider dynamic incoming requests, in which their arrival and holding times are not known in advance. The objective is to devise a strategy that minimizes the number of rejected packages due to the lack of resources in the long term. We use graph neural networks to capture crucial latent information from the graph-structured input to develop the optimal strategy. The proposed deep reinforcement learning algorithm selects a route and a wavelength simultaneously for each incoming traffic connection as they arrive. The results demonstrate that the learned agent outperforms the methods used in practice and can be generalized on network topologies that did not participate in training.
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights
Simon Bowly
Jonas Charfreitag
Didier Chételat
Antonia Chmiela
Justin Dumouchelle
Ambros Gleixner
Aleksandr Kazachkov
Elias Boutros Khalil
Paweł Lichocki
Miles Lubin
Chris J. Maddison
Christopher Morris
D. Papageorgiou
Augustin Parjadis
Sebastian Pokutta
Antoine Prouvost … (see 22 more)
Lara Scavuzzo
Giulia Zarpellon
Linxin Yangm
Sha Lai
Akang Wang
Xiaodong Luo
Xiang Zhou
Haohan Huang
Sheng Cheng Shao
Yuanming Zhu
Dong Dong Zhang
Tao Manh Quan
Zixuan Cao
Yang Xu
Zhewei Huang
Shuchang Zhou
C. Binbin
He Minggui
Haoren Ren Hao
Zhang Zhiyu
An Zhiwu
Mao Kun
Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have focused … (see more)on solving problem instances in isolation, ignoring that they often stem from related data distributions in practice. However, recent years have seen a surge of interest in using machine learning as a new approach for solving combinatorial problems, either directly as solvers or by enhancing exact solvers. Based on this context, the ML4CO aims at improving state-of-the-art combinatorial optimization solvers by replacing key heuristic components. The competition featured three challenging tasks: finding the best feasible solution, producing the tightest optimality certificate, and giving an appropriate solver configuration. Three realistic datasets were considered: balanced item placement, workload apportionment, and maritime inventory routing. This last dataset was kept anonymous for the contestants.