Yoshua Bengio

jamal.abouhaibeh@mila.quebec

Biography

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Yoshua Bengio is recognized worldwide as a leading expert in AI. He is most known for his pioneering work in deep learning, which earned him the 2018 A.M. Turing Award, “the Nobel Prize of computing,” with Geoffrey Hinton and Yann LeCun.

Bengio is a full professor at Université de Montréal, and the founder and scientific advisor of Mila – Quebec Artificial Intelligence Institute. He is also a senior fellow at CIFAR and co-directs its Learning in Machines & Brains program, serves as special advisor and founding scientific director of IVADO, and holds a Canada CIFAR AI Chair.

In 2019, Bengio was awarded the prestigious Killam Prize and in 2022, he was the most cited computer scientist in the world by h-index. He is a Fellow of the Royal Society of London, Fellow of the Royal Society of Canada, Knight of the Legion of Honor of France and Officer of the Order of Canada. In 2023, he was appointed to the UN’s Scientific Advisory Board for Independent Advice on Breakthroughs in Science and Technology.

Concerned about the social impact of AI, Bengio helped draft the Montréal Declaration for the Responsible Development of Artificial Intelligence and continues to raise awareness about the importance of mitigating the potentially catastrophic risks associated with future AI systems.

Current Students

Jamal Abou Haibeh

Collaborating Alumni - McGill University

Berkes Anaïs

Collaborating researcher - Cambridge University

Principal supervisor :

berkes.anais@mila.quebec

Rim Assouel

PhD - Université de Montréal

assouelr@mila.quebec

Shahana Chatterjee

Collaborating researcher - N/A

Principal supervisor :

shahana.chatterjee@mila.quebec

sanghyeok.choi@mila.quebec

Xiaoyin Chen

PhD - Université de Montréal

xiaoyin.chen@mila.quebec

Sanghyeok Choi

Collaborating researcher - KAIST

PhD - Université de Montréal

aniket.didolkar@mila.quebec

PhD - Université de Montréal

Co-supervisor :

Guillaume Lajoie

eric.elmoznino@mila.quebec

PhD - Université de Montréal

lena-nehale.ezzine@mila.quebec

Jean-Pierre Falet

PhD - Université de Montréal

jean-pierre.falet@mila.quebec

PhD

pietro.greiner@mila.quebec

Mohsin Hasan

PhD - Université de Montréal

mohsin.hasan@mila.quebec

thomas.jiralerspong@mila.quebec

Moksh Jain

PhD - Université de Montréal

moksh.jain@mila.quebec

PhD - Université de Montréal

Principal supervisor :

Guillaume Lajoie

Collaborating Alumni - Université de Montréal

younesse.kaddar@mila.quebec

Minsu Kim

Collaborating researcher - Université de Montréal

minsu.kim@mila.quebec

Hyeonah Kim

Postdoctorate - Université de Montréal

Principal supervisor :

hyeonah.kim@mila.quebec

Jeongjae Lee

Research Intern - KOREA ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY

jeongjae.lee@mila.quebec

Collaborating researcher

Song LIU

Collaborating researcher - s.o.

liusong299@gmail.com

cristian-dragos.manta@mila.quebec

Cristian Dragos Manta

PhD - Université de Montréal

Co-supervisor :

Dhanya Sridhar

Sarthak Mittal

PhD - Université de Montréal

Principal supervisor :

Independent visiting researcher - Université de Montréal

nguyenph@mila.quebec

Ali Parviz

Collaborating researcher - Ying Wu Coll of Computing

ali.parviz@mila.quebec

Lena Podina

Collaborating researcher - University of Waterloo

Principal supervisor :

elena.podina@mila.quebec

jarrid.rector-brooks@mila.quebec

Jarrid Rector-Brooks

PhD - Université de Montréal

Danyal REHMAN

Postdoctorate - Université de Montréal

danyal.rehman@mila.quebec

oliver.richardson@mila.quebec

Oli RICHARDSON

Postdoctorate - Université de Montréal

Camille Rochefort-Boulanger

PhD - Université de Montréal

Principal supervisor :

Postdoctorate - Université de Montréal

Co-supervisor :

divya.sharma@mila.quebec

Mélisande Astrid Crystal Teng

Collaborating Alumni - Université de Montréal

Co-supervisor :

Alex Tong

Collaborating Alumni - Université de Montréal

alexander.tong@mila.quebec

Collaborating researcher - University of Cambridge

oszkar.urban@mila.quebec

Donna Vakalis

Collaborating Alumni - Université de Montréal

Co-supervisor :

donna.vakalis@mila.quebec

PhD - Université de Montréal

Principal supervisor :

Glen Berseth

siddarth.venkatraman@mila.quebec

Collaborating researcher - Université de Montréal

esmeralda.whitammer@mila.quebec

Collaborating Alumni

omar.younis@mila.quebec

Collaborating researcher - Université de Montréal

taeyoung.yun@mila.quebec

Skipper: Combining Spatial and Temporal Abstraction for Better Generalization

Tianyu Zhang

PhD - Université de Montréal

tianyu.zhang@mila.quebec

PhD - McGill University

Principal supervisor :

Blog Posts

Generic thumbnail for Mila Blog articles.

February 22, 2024

Mingde Harry Zhao

Safa Alver

Harm van Seijen

Romain Laroche

Doina Precup

Yoshua Bengio

Scaling in the Service of Reasoning & Model-Based ML

April 4, 2023

Yoshua Bengio

Edward J. Hu

A collaboration between Mila and Relation Therapeutics to discover novel synergistic combinations of drugs in vitro

March 23, 2022

Paul Bertin

Jake P. Taylor-King

Yoshua Bengio

March 15, 2022

Generative Flow Networks

Yoshua Bengio

Publications

Insuring AI: Incentivising Safe and Secure Deployment

Agni Orfanoudaki

Carsten Maple

Matthew Wicker

Kwok-Yan Lam

Marcin Detyniecki

Lukasz Szpruch

2026-07-19

Harvard Data Science Review (published)

The 2026 Singapore Consensus on Global AI Safety Research Priorities

Mohan Kankanhalli

Lee Wan Sie

Tegan Maharaj

Chris Meserole

Luke Ong

Stuart Russell

Dawn Song

Max Tegmark

Brian Tse

Xue Lan

Andrew Yao

Zhang Ya-Qin

Zhou Bowen

Stephen Casper

Sören Mindermann

Oskar Galeev

Ima (Imane) Bello

Kwan Yee Ng

Vanessa Wilfred … (see 100 more)

Erica Liaw

Lee Chein Inn

Lin Wanxuan

Ng En Qi

Jonathan Lee

José Villalobos

Abhishek Aggarwal

Adam Gleave

Alan Chan

Alex Leung

Alvin Kwock

Anthony Tung

Arisa Siong

Arthur Tea

BEN BUCKNALL

Benjamin Weinstein-Raun

He Bing Sheng

Liu Bo

Bryan Kian Hsiang Low

Chris Ngo

Clement Neo

Cyrus Hodes

Dan Hendrycks

Daniel Ross

Liu Dapeng

Denise Wong

Djordje Žikelić

Elham Tabassi

Fabien Le Voyer

Fazl Barez

Gabriel Nicholas

Henry Papadatos

Jaan Tallinn

James Petrie

Xu Jia

Shao Jing

Jonathan Barry

Julia Chen

Sun Jun

Karson Elmgren

Kat Lyness

Katherine Lee

Kristy Loke

Lee Kwee Geak

Leslie Teo

Meng Ling Yu

Lisa Soder

Madhulika Srikumar

Malcolm Murray

Mark Brakel

Mark Nitzberg

Mary Phuong

Matthew Jagielski

Max Fenkell

Miro Plueckebaum

Kim Myuhng Joo

Hu Naying

Neil Davison

Nicolas Miailhe

Niki Iliadis

Nur Syahidah Sahrom

Ong Chen Hui

Pradeep Varakantham

Rebecca Finlay

Renata Dwan

Robert Opp

Rumman Chowdhury

Saad Siddiqui

Sabina Nong

Sam Ramadori

Sami Jawhar

Samuel Boger

Sara Hooker

Ying Shao Wei

Sebastian Hallensleben

Shinyuk Kang

Sophie Toura

Sreejith Balakrishnan

Stephanie Kasaon

Stephen Clare

Summer Yue

Sunny Yuqing Sun

Supheakmungkol Sarin

Tian Tian

Tim Schreier

Tori Westerhoff

Urvashi Aneja

Wayne Tee

Lu Wei

Xu Wei

Zhang Wenxuan

Hu Xia

Yang Xiaofang

Pan Xudong

Xiao Yajun

Yifan Jia

Tan Yong Khiam

Yuejin Du

Yuma Kurihara

Tan Zhi Xuan

Frontier AI capabilities and autonomy are advancing rapidly. A growing number of real-world incidents make a trusted AI ecosystem essential … (see more)to embracing AI with confidence. The 2026 Singapore Consensus is an outcome of the second International Scientific Exchange on AI Safety, bringing together over 100 contributors spanning 13 countries from frontier developers, government safety institutes, academia, and civil society. Building on the 2025 report, it presents a global understanding of technical AI safety research problems of top priority, now with a dedicated focus on societal resilience and on managing the risks of increasingly autonomous AI agents.

2026-07-08

arXiv (preprint)

Safety from Honesty in a Disinterested AI Predictor

Oliver Richardson

Tomáš Gavenčiak

Michael Cohen

Rory Svarc

Damiano Fornasiere

Gaël Gendron

David Hyland

Aton Kamanda

Adam Oberman

Francis Rhys Ward

Anna Gavenčiak

Jacob Livingston Slosser

Vincent Mai

Iulian Serban

Joumana Ghosn

As AI systems become more capable, training procedures that optimize for downstream outcomes risk introducing implicit agency: goal-directed… (see more) behavior that designers never specified. We present a formal safety argument for the Scientist AI (SAI) Predictor, trained to approximate the Bayesian posterior conditioned on a dataset of"epistemically contextualized"natural-language statements. We argue that such a Predictor can honestly predict agents, actions, and their consequences without itself being an agent that selects outputs to achieve goals. This rests on data representation and on the training procedure. Epistemic contextualization of text distinguishes latent factual claims from communication acts, so expressions of goals are treated as evidence to be explained rather than drives the model adopts. With a posterior-seeking training objective, this is intended to drive the Predictor toward calibrated, cautious predictions. Training proceeds so downstream effects of deploying a prediction never serve as a reward signal; any agency the system needs is supplied by explicit scaffolding constrained by guardrails. We prove that, under assumptions on the training dynamics and on the argued sparsity of dangerous Predictors, the probability that training produces a Predictor whose guarded deployment carries residual harm above a specified threshold is small: a dangerous Predictor would have to underestimate harm in a coordinated way across many queries while such coordinated patterns are rare under the initialization distribution and receive no direct training signal. Safety and accuracy are jointly supported in this framework, since the constraints that secure accuracy are the same ones that make coordinated deception costly. These guarantees against misalignment and agency arising from within the Predictor itself do not preclude the use of the Predictor as part of an agentic system.

2026-06-27

arXiv (preprint)

Bayesian Decision-Time Inference for In-Context Reinforcement Learning from Suboptimal Data

Anaïs Berkes

In-context reinforcement learning (ICRL) promises rapid adaptation without parameter updates, but standard supervised objectives often fail … (see more)when pretraining data is generated by suboptimal behaviour policies. In these regimes, logged actions are unreliable labels while rewards still provide value-relevant information. To address this, we introduce SPICE, a Bayesian decision-time inference method that shifts online ICRL from action-logit prediction to approximate posterior inference over action values, requiring neither expert action labels nor algorithmic learning traces. SPICE learns a task-conditioned value prior with a transformer value ensemble and, at test time with parameters frozen, fuses this prior with kernel-weighted context evidence via a closed-form Gaussian fusion update. The resulting estimates drive a posterior-UCB controller, enabling principled online exploration and adaptation without gradient updates. A stochastic-bandit analysis shows logarithmic regret growth for the fixed-prior controller under scheduled exploration, while quantifying the additional early cost caused by inaccurate prior estimates. Across bandits, Darkroom, image-based MiniWorld, and continuous building control, SPICE adapts more effectively from suboptimal data than supervised ICRL baselines.

2026-06-15

ContinualRL @ Reinforcement Learning Conference (published)

CIAware-Bench: Benchmarking Control Intervention Awareness Across Frontier LLMs

Joachim Schaeffer

Thomas Jiralerspong

Alexander Panfilov

Guillaume Lajoie

Jonas Geiping

Roland S. Zimmermann

AI control protocols oversee untrusted models by monitoring their actions and modifying potentially unsafe steps, often using a trusted mode… (see more)l. This partially tampers with the untrusted model's trajectory. If the trusted model detects such an intervention, it may infer properties of the monitor and adapt to evade control. We introduce \textbf{CIAware-Bench}, a benchmark for measuring \textbf{c}ontrol \textbf{i}ntervention (CI) awareness across frontier models. CIAware-Bench tests whether models can distinguish their own trajectories from those modified by a control intervention. The benchmark is comprised of a suite of four task domains (essay writing, BigCodeBench, Bash Arena, and SHADE-Arena), while varying trajectory watermarking, side-task presence, and the control protocol. Evaluating eleven frontier models, we find low to moderate CI awareness under default settings (up to 0.87; random chance balanced binary classification accuracy is 0.5) with substantial variation across task domains and model pairs. Detection is generally easier across model families, suggesting that models exploit provider-specific differences in style or post-training. Overall, CI awareness is not a fixed model-level property, and should be measured for each new model release and deployment scenario. We release CIAware-Bench to track CI awareness and inform control protocols whose interventions are harder to detect.

2026-06-08

arXiv (preprint)

Noticing the Watcher: LLM Agents Can Infer CoT Monitoring from Blocking Feedback

Thomas Jiralerspong

Flemming Kondrup

Chain-of-thought (CoT) monitoring provides oversight into model reasoning, but its effectiveness assumes models do not know they are being w… (see more)atched. We ask whether reasoning agents can autonomously infer that their supposedly private chain of thought is under surveillance, and whether this awareness leads to strategic evasion, without any explicit training or instructions to do so. In a multi-episode agentic framework, models pursue both a primary task and a concealed side task while being told their reasoning is private; a hidden CoT monitor blocks episodes when suspicious reasoning is detected. We find that frontier models can deduce the existence of this monitor purely from blocking feedback, with the most capable models reaching confident belief that their thinking is observed in up to 19\% of episodes. This awareness scales with model capability and, in rare cases, escalates to explicit intent to suppress reasoning about the side task. However, models that form this intent uniformly fail to execute it, openly reasoning about their concealed objectives in the very next episode. This intent–capability gap is reassuring for current deployment, but the autonomous emergence of both monitoring awareness and evasion intent suggests that CoT monitoring is not a permanently reliable safeguard.

2026-06-03

PhilML @ International Conference on Machine Learning (poster)

Bayesian Symbolic Regression with Entropic Reinforcement Learning

Sida Li

Esmeralda S. Whitammer

Symbolic regression is the problem of finding an algebraic expression describing a stochastic dependence of a target variable on a set of in… (see more)puts. Unlike forms of regression that fit parameters assuming a fixed model structure, symbolic regression is a search problem over the space of expressions, represented, for example, as abstract syntax trees using a library of operators. Symbolic regression is typically used in settings with limited, noisy data in the natural sciences. However, searching for a single best-fitting expression fails to capture the epistemic uncertainty about the expression, which motivates a Bayesian perspective that enables uncertainty quantification and specification of natural priors to constrain the search space. In this work, we propose ERRLESS (Entropy-Regularized Reinforcement Learning for Expression Structure Sampling), a scalable approach for sampling from the posterior distribution over expressions given data using maximum-entropy reinforcement learning. ERRLESS learns a neural policy that constructs expressions sequentially by building up their abstract syntax trees. At convergence, the policy samples expressions from the posterior. At test time, expressions can be sampled by rollouts of this policy. We demonstrate that ERRLESS achieves competitive results on the Feynman benchmark while producing short and interpretable expressions. Additionally, we demonstrate that the mean of the posterior predictive approximated by ERRLESS achieves a high coefficient of determination (

2026-05-30

Conference on Uncertainty in Artificial Intelligence (poster)

proceedings.mlr.press

Generative Recursive Reasoning

Mingyu Jo

Mengye Ren

How should future neural reasoning systems implement extended computation? Recursive Reasoning Models (RRMs) offer a promising alternative t… (see more)o autoregressive sequence extension by performing iterative latent-state refinement with shared transition functions. Yet existing RRMs are largely deterministic, following a single latent trajectory and converging to a single prediction. We introduce Generative Recursive reAsoning Models (GRAM), a framework that turns recursive latent reasoning into probabilistic multi-trajectory computation. GRAM models reasoning as a stochastic latent trajectory, enabling multiple hypotheses, alternative solution strategies, and inference-time scaling through both recursive depth and parallel trajectory sampling. This yields a latent-variable generative model supporting conditional reasoning via

2026-05-18

arXiv (preprint)

Synthesizable Molecular Generation via Soft-constrained GFlowNets with Rich Chemical Priors

Louis Vaillancourt

Yves V. Brun

2026-04-29

International Conference on Machine Learning (accepted)

When 2D Tasks Meet 1D Serialization: On Serialization Friction in Structured Tasks

C.-T.D. Lo

Lu Li

Diji Yang

Tianyu Zhang

Yunkai Zhang

Yi Zhang

Chung-Hsiang Lo

In the LLM era, many symbolic and structured problems are presented to models through 1D text serialization. Yet some such problems are nati… (see more)vely two-dimensional: their relevant relations, such as row--column correspondence or spatial adjacency, are defined by position in a 2D layout rather than by sequential order. This raises a representational question: does preserving the same symbolic entries in a 1D sequence also preserve the relational structure needed for computation? We study this issue through the lens of serialization friction: the representational mismatch in which the same underlying task instances and entries are still present, but relations that depend on layout become implicit under 1D serialization. The study uses a controlled synthetic testbed of three tasks: matrix transpose, Conway's Game of Life, and LU decomposition. In each task, the same instances are presented either as 1D text serialization or as their native 2D layout rendered as an image. Across this testbed, 1D serialization degrades more sharply as task size grows, and errors under serialization exhibit spatially structured patterns, suggesting that this presentation choice is consequential within our testbed. To further interpret these results, we add supplementary analyses that include a within-visual probe and an additional comparison of the two input presentations under the mixed-training transpose setting. These findings suggest that, for layout-defined tasks, reducing inputs to 1D serialization is not a neutral choice of representation.

2026-04-28

arXiv (preprint)

Catalyst GFlowNet for electrocatalyst design: A hydrogen evolution reaction case study

Lena Podina

Efficient and inexpensive energy storage is essential for accelerating the adoption of renewable energy and ensuring a stable supply, despit… (see more)e fluctuations in sources such as wind and solar. Electrocatalysts play a key role in hydrogen energy storage (HES), allowing the energy to be stored as hydrogen. However, the development of affordable and high-performance catalysts for this process remains a significant challenge. We introduce Catalyst GFlowNet, a generative model that leverages machine learning-based predictors of formation and adsorption energy to design crystal surfaces that act as efficient catalysts. We demonstrate the performance of the model through a proof-of-concept application to the hydrogen evolution reaction, a key reaction in HES, for which we successfully identified platinum as the most efficient known catalyst. In future work, we aim to extend this approach to the oxygen evolution reaction, where current optimal catalysts are expensive metal oxides, and open the search space to discover new materials. This generative modeling framework offers a promising pathway for accelerating the search for novel and efficient catalysts.

2026-04-23

OpenReview (published)

General Multimodal Protein Design Enables DNA-Encoding of Chemistry

Jarrid Rector-Brooks

Théophile Lambert

Marta Skreta

Daniel Roth

Yueming Long

Zi-Qi Li

Xi Zhang

Miruna Cretu

Francesca-Zhoufan Li

Tanvi Ganapathy

Emily Jin

Avishek Joey Bose

Jason Yang

Kirill Neklyudov

Alexander Tong

Frances H. Arnold

Cheng-Hao Liu

Evolution is an extraordinary engine for enzymatic diversity, yet the chemistry it has explored remains a narrow slice of what DNA can encod… (see more)e. Deep generative models can design new proteins that bind ligands, but none have created enzymes without pre-specifying catalytic residues. We introduce DISCO (DIffusion for Sequence-structure CO-design), a multimodal model that co-designs protein sequence and 3D structure around arbitrary biomolecules, as well as inference-time scaling methods that optimize objectives across both modalities. Conditioned solely on reactive intermediates, DISCO designs diverse heme enzymes with novel active-site geometries. These enzymes catalyze new-to-nature carbene-transfer reactions, including alkene cyclopropanation, spirocyclopropanation, B-H, and C(sp

2026-04-05

arXiv (preprint)