Publications

The BrowserGym Ecosystem for Web Agent Research
Thibault Le Sellier De Chezelles
Maxime Gasse
Alexandre Lacoste
Alexandre Drouin
Massimo Caccia
Léo Boisvert
Megh Thakkar
Tom Marty
Rim Assouel
Sahar Omidi Shayegan
Lawrence Keunho Jang
Xing Han Lu
Ori Yoran
Dehan Kong
Frank F. Xu
Siva Reddy
Quentin Cappart
Graham Neubig
Russ Salakhutdinov
Nicolas Chapados
The BrowserGym ecosystem addresses the growing need for efficient evaluation and benchmarking of web agents, particularly those leveraging a… (voir plus)utomation and Large Language Models (LLMs) for web interaction tasks. Many existing benchmarks suffer from fragmentation and inconsistent evaluation methodologies, making it challenging to achieve reliable comparisons and reproducible results. BrowserGym aims to solve this by providing a unified, gym-like environment with well-defined observation and action spaces, facilitating standardized evaluation across diverse benchmarks. Combined with AgentLab, a complementary framework that aids in agent creation, testing, and analysis, BrowserGym offers flexibility for integrating new benchmarks while ensuring consistent evaluation and comprehensive experiment management. This standardized approach seeks to reduce the time and complexity of developing web agents, supporting more reliable comparisons and facilitating in-depth analysis of agent behaviors, and could result in more adaptable, capable agents, ultimately accelerating innovation in LLM-driven automation. As a supporting evidence, we conduct the first large-scale, multi-benchmark web agent experiment and compare the performance of 6 state-of-the-art LLMs across all benchmarks currently available in BrowserGym. Among other findings, our results highlight a large discrepancy between OpenAI and Anthropic's latests models, with Claude-3.5-Sonnet leading the way on almost all benchmarks, except on vision-related tasks where GPT-4o is superior. Despite these advancements, our results emphasize that building robust and efficient web agents remains a significant challenge, due to the inherent complexity of real-world web environments and the limitations of current models.
2025-01-01
Trans. Mach. Learn. Res. (publié)
doi.org
openreview.net
On the compatibility of generative AI and generative linguistics
Eva Portelance
Masoud Jasbi
2025-01-01
Nat. Comput. Sci. (publié)
doi.org
arxiv.org
The Normative Leadership of the World Health Organization : a quantitative analysis 
Gaëlle Foucault
Catherine Régis
Jean-Louis Denis
Pierre Larouche
Miriam Cohen
2025-01-01
SSRN Electronic Journal (publié)
doi.org
The Normative Leadership of the World Health Organization : a quantitative analysis 
Gaëlle Foucault
Catherine Régis
Jean-Louis Denis
Pierre Larouche
Miriam Cohen
2025-01-01
Social Science Research Network (publié)
doi.org
The Normative Leadership of the World Health Organization : a quantitative analysis 
Gaëlle Foucault
Catherine Régis
Jean-Louis Denis
Pierre Larouche
Miriam Cohen
2025-01-01
Social Science Research Network (publié)
doi.org
The role of AI for MRI-analysis in multiple sclerosis—A brief overview
Jean-Pierre R. Falet
Steven Nobile
Aliya Szpindel
Berardino Barile
Amar Kumar
Joshua D. Durso-Finley
Tal Arbel
Douglas Arnold
2025-01-01
Frontiers Artif. Intell. (publié)
doi.org
The Singapore Consensus on Global AI Safety Research Priorities
Yoshua Bengio
Tegan Maharaj
Luke Ong
Stuart Russell
Dawn Song
Max Tegmark
Lan Xue
Ya-Qin Zhang
Stephen Casper
Wan Sie Lee
Sören Mindermann
Vanessa Wilfred
Vidhisha Balachandran
Fazl Barez
Michael Belinsky
Imane Bello
Malo Bourgon
Mark Brakel
Sim'eon Campos
Duncan Cass-Beggs … (voir 67 de plus)
Jiahao Chen
Rumman Chowdhury
Kuan Chua Seah
Jeff Clune
Juntao Dai
Agnès Delaborde
Nouha Dziri
Francisco Eiras
Joshua Engels
Jinyu Fan
Adam Gleave
Noah D. Goodman
Fynn Heide
Johannes Heidecke
Dan Hendrycks
Cyrus Hodes
Bryan Low Kian Hsiang
Minlie Huang
Sami Jawhar
Jingyu Wang
Adam Tauman Kalai
Meindert Kamphuis
Mohan S. Kankanhalli
Subhash Kantamneni
Mathias Bonde Kirk
Thomas Kwa
Jeffrey Ladish
Kwok-Yan Lam
Wan Lee Sie
Taewhi Lee
Xiaojian Li
Jiajun Liu
Chaochao Lu
Yifan Mai
Richard Mallah
Julian Michael
Nick Moës
Simon Möller
Kihyuk Nam
Kwan Yee Ng
Mark Nitzberg
Besmira Nushi
Sean O hEigeartaigh
Alejandro Ortega
Pierre Peigné
James Petrie
Benjamin Prud'homme
Reihaneh Rabbany
Nayat Sanchez-Pi
Sarah Schwettmann
Buck Shlegeris
Saad Siddiqui
Aradhana Sinha
Martín Soto
Cheston Tan
Dong Ting
William Tjhi
Robert Trager
Brian Tse
H. AnthonyTungK.
John Willes
Denise Wong
Wei Xu
Rongwu Xu
Yi Zeng 0005
HongJiang Zhang
Djordje Zikelic
2025-01-01
arXiv.org (prépublication)
doi.org
The Singapore Consensus on Global AI Safety Research Priorities
Yoshua Bengio
Tegan Maharaj
Luke Ong
Stuart Russell
Dawn Song
Max Tegmark
Lan Xue
Ya-Qin Zhang
Stephen Casper
Wan Sie Lee
Sören Mindermann
Vanessa Wilfred
Vidhisha Balachandran
Fazl Barez
Michael Belinsky
Imane Bello
Malo Bourgon
Mark Brakel
Sim'eon Campos
Duncan Cass-Beggs … (voir 67 de plus)
Jiahao Chen
Rumman Chowdhury
Kuan Chua Seah
Jeff Clune
Juntao Dai
Agnès Delaborde
Nouha Dziri
Francisco Eiras
Joshua Engels
Jinyu Fan
Adam Gleave
Noah D. Goodman
Fynn Heide
Johannes Heidecke
Dan Hendrycks
Cyrus Hodes
Bryan Low Kian Hsiang
Minlie Huang
Sami Jawhar
Jingyu Wang
Adam Tauman Kalai
Meindert Kamphuis
Mohan S. Kankanhalli
Subhash Kantamneni
Mathias Bonde Kirk
Thomas Kwa
Jeffrey Ladish
Kwok-Yan Lam
Wan Lee Sie
Taewhi Lee
Xiaojian Li
Jiajun Liu
Chaochao Lu
Yifan Mai
Richard Mallah
Julian Michael
Nick Moës
Simon Möller
Kihyuk Nam
Kwan Yee Ng
Mark Nitzberg
Besmira Nushi
Sean O hEigeartaigh
Alejandro Ortega
Pierre Peigné
James Petrie
Benjamin Prud'homme
Reihaneh Rabbany
Nayat Sanchez-Pi
Sarah Schwettmann
Buck Shlegeris
Saad Siddiqui
Aradhana Sinha
Martín Soto
Cheston Tan
Dong Ting
William-Chandra Tjhi
Robert Trager
Brian Tse
H. AnthonyTungK.
John Willes
Denise Wong
W. Xu
Rongwu Xu
Yi Zeng
HongJiang Zhang
Djordje Zikelic
Rapidly improving AI capabilities and autonomy hold significant promise of transformation, but are also driving vigorous debate on how to en… (voir plus)sure that AI is safe, i.e., trustworthy, reliable, and secure. Building a trusted ecosystem is therefore essential -- it helps people embrace AI with confidence and gives maximal space for innovation while avoiding backlash. The "2025 Singapore Conference on AI (SCAI): International Scientific Exchange on AI Safety" aimed to support research in this space by bringing together AI scientists across geographies to identify and synthesise research priorities in AI safety. This resulting report builds on the International AI Safety Report chaired by Yoshua Bengio and backed by 33 governments. By adopting a defence-in-depth model, this report organises AI safety research domains into three types: challenges with creating trustworthy AI systems (Development), challenges with evaluating their risks (Assessment), and challenges with monitoring and intervening after deployment (Control).
2025-01-01
arXiv.org (prépublication)
doi.org
arxiv.org
The Size of Teachers as a Measure of Data Complexity: PAC-Bayes Excess Risk Bounds and Scaling Laws
Gintare Karolina Dziugaite
Daniel M. Roy
We study the generalization properties of randomly initialized neural networks, under the assumption that the network is larger than some un… (voir plus)known "teacher" network that achieves low risk. We extend the analysis of Buzaglo et al. (2024) to allow for student networks of arbitrary width and depth, and to the setting where no (small) teacher network perfectly interpolates the data. We obtain an oracle inequality, relating the risk of Gibbs posterior sampling to that of narrow teacher networks. As a result, the sample complexity is once again bounded in terms of the size of narrow teacher networks that themselves achieve small risk. We then introduce a new notion of data complexity, based on the minimal size of a teacher network required to achieve a certain level of excess risk. By comparing the scaling laws resulting from our bounds to those observed in empirical studies, we are able to estimate the data complexity of standard benchmarks according to our measure.
2025-01-01
AISTATS (publié)
proceedings.mlr.press
openreview.net
The Superposition of Diffusion Models Using the Itô Density Estimator
Marta Skreta
Lazar Atanackovic
Joey Bose
Alexander Tong
Kirill Neklyudov
The Cambrian explosion of easily accessible pre-trained diffusion models suggests a demand for methods that combine multiple different pre-t… (voir plus)rained diffusion models without incurring the significant computational burden of re-training a larger combined model. In this paper, we cast the problem of combining multiple pre-trained diffusion models at the generation stage under a novel proposed framework termed superposition. Theoretically, we derive superposition from rigorous first principles stemming from the celebrated continuity equation and design two novel algorithms tailor-made for combining diffusion models in SuperDiff. SuperDiff leverages a new scalable It\^o density estimator for the log likelihood of the diffusion SDE which incurs no additional overhead compared to the well-known Hutchinson's estimator needed for divergence calculations. We demonstrate that SuperDiff is scalable to large pre-trained diffusion models as superposition is performed solely through composition during inference, and also enjoys painless implementation as it combines different pre-trained vector fields through an automated re-weighting scheme. Notably, we show that SuperDiff is efficient during inference time, and mimics traditional composition operators such as the logical OR and the logical AND. We empirically demonstrate the utility of using SuperDiff for generating more diverse images on CIFAR-10, more faithful prompt conditioned image editing using Stable Diffusion, as well as improved conditional molecule generation and unconditional de novo structure design of proteins. https://github.com/necludov/super-diffusion
2025-01-01
ICLR (publié)
doi.org
arxiv.org
Towards contrast-agnostic soft segmentation of the spinal cord
Sandrine Bédard
Enamundram Naga Karthik
Charidimos Tsagkas
Emanuele Pravatà
Cristina Granziera
Andrew C. Smith
Kenneth Arnold Weber
Julien Cohen-Adad
Spinal cord segmentation is clinically relevant and is notably used to compute spinal cord cross-sectional area (CSA) for the diagnosis and … (voir plus)monitoring of cord compression or neurodegenerative diseases such as multiple sclerosis. While several semi and automatic methods exist, one key limitation remains: the segmentation depends on the MRI contrast, resulting in different CSA across contrasts. This is partly due to the varying appearance of the boundary between the spinal cord and the cerebrospinal fluid that depends on the sequence and acquisition parameters. This contrast-sensitive CSA adds variability in multi-center studies where protocols can vary, reducing the sensitivity to detect subtle atrophies. Moreover, existing methods enhance the CSA variability by training one model per contrast, while also producing binary masks that do not account for partial volume effects. In this work, we present a deep learning-based method that produces soft segmentations of the spinal cord. Using the Spine Generic Public Database of healthy participants (
2025-01-01
Medical Image Anal. (publié)
doi.org
arxiv.org
Trained Without My Consent: Detecting Code Inclusion In Language Models Trained on Code
Vahid Majdinasab
Amin Nikanjam
Foutse Khomh
Code auditing ensures that the developed code adheres to standards, regulations, and copyright protection by verifying that it does not cont… (voir plus)ain code from protected sources. The recent advent of Large Language Models (LLMs) as coding assistants in the software development process poses new challenges for code auditing. The dataset for training these models is mainly collected from publicly available sources. This raises the issue of intellectual property infringement as developers' codes are already included in the dataset. Therefore, auditing code developed using LLMs is challenging, as it is difficult to reliably assert if an LLM used during development has been trained on specific copyrighted codes, given that we do not have access to the training datasets of these models. Given the non-disclosure of the training datasets, traditional approaches such as code clone detection are insufficient for asserting copyright infringement. To address this challenge, we propose a new approach, TraWiC; a model-agnostic and interpretable method based on membership inference for detecting code inclusion in an LLM's training dataset. We extract syntactic and semantic identifiers unique to each program to train a classifier for detecting code inclusion. In our experiments, we observe that TraWiC is capable of detecting 83.87% of codes that were used to train an LLM. In comparison, the prevalent clone detection tool NiCad is only capable of detecting 47.64%. In addition to its remarkable performance, TraWiC has low resource overhead in contrast to pair-wise clone detection that is conducted during the auditing process of tools like CodeWhisperer reference tracker, across thousands of code snippets.
2025-01-01
ACM Trans. Softw. Eng. Methodol. (publié)
doi.org
arxiv.org