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

Collaborating researcher - Cambridge University

Principal supervisor :

David Rolnick

berkes.anais@mila.quebec

sanghyeok.choi@mila.quebec

Rim Assouel

PhD - Université de Montréal

assouelr@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

Hyeonah Kim

Postdoctorate - Université de Montréal

Principal supervisor :

Alex Hernández-García

hyeonah.kim@mila.quebec

Minsu Kim

Collaborating researcher - Université de Montréal

minsu.kim@mila.quebec

Collaborating researcher - KOREA ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY

jeongjae.lee@mila.quebec

cristian-dragos.manta@mila.quebec

Song LIU

Collaborating researcher - s.o.

Collaborating researcher

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 :

David Rolnick

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 :

Alex Hernández-García

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@mila.quebec

Donna Vakalis

Collaborating Alumni - Université de Montréal

Co-supervisor :

David Rolnick

PhD - Université de Montréal

Principal supervisor :

Glen Berseth

siddarth.venkatraman@mila.quebec

Collaborating researcher

joseph.viviano2@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 :

Harry Zhao

Collaborating Alumni - 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

Scaling Latent Reasoning via Looped Language Models

Ruiming Zhu

Zixuan Wang

Kai Hua

Tianyu Zhang

Ziniu Li

Haoran Que

Boyi Wei

Zixin Wen

Fan Yin

He Xing

Li Li

Jiajun Shi

Kaijing Ma

Shanda Li

Taylor Kergan

Andrew C. Smith

Xin Qu

Mude Hui

Bohong Wu

Qiyang Min … (see 13 more)

Hongzhi Huang

Xun Zhou

Wei Ye

Jiaheng Liu

Jian Yang 0030

Yunfeng Shi

Chenghua Lin

Enduo Zhao

Tianle Cai

Ge Zhang

Wenhao Huang

Jason K. Eshraghian

Modern LLMs are trained to"think"primarily via explicit text generation, such as chain-of-thought (CoT), which defers reasoning to post-trai… (see more)ning and under-leverages pre-training data. We present and open-source Ouro, named after the recursive Ouroboros, a family of pre-trained Looped Language Models (LoopLM) that instead build reasoning into the pre-training phase through (i) iterative computation in latent space, (ii) an entropy-regularized objective for learned depth allocation, and (iii) scaling to 7.7T tokens. Ouro 1.4B and 2.6B models enjoy superior performance that match the results of up to 12B SOTA LLMs across a wide range of benchmarks. Through controlled experiments, we show this advantage stems not from increased knowledge capacity, but from superior knowledge manipulation capabilities. We also show that LoopLM yields reasoning traces more aligned with final outputs than explicit CoT. We hope our results show the potential of LoopLM as a novel scaling direction in the reasoning era. Our model is available here: http://ouro-llm.github.io.

2025-10-28

ArXiv (preprint)

Deep-learning-based virtual screening of antibacterial compounds

Gabriele Scalia

Steven T. Rutherford

Ziqing Lu

Kerry R. Buchholz

Nicholas Skelton

Kangway Chuang

Nathaniel Diamant

Jan-Christian Hütter

Jerome-Maxim Luescher

Anh Miu

Jeff Blaney

Leo Gendelev

Elizabeth Skippington

Greg Zynda

Nia Dickson

Michał Koziarski

Aviv Regev

Man-Wah Tan

Tommaso Biancalani

2025-10-23

Nature Biotechnology (unknown)

Surrogate-based quantification of policy uncertainty in generative flow networks

Ram'on Nartallo-Kaluarachchi

Robert Manson-Sawko

Shashanka Ubaru

Dongsung Huh

Malgorzata J. Zimo'n

Lior Horesh

2025-10-23

ArXiv (preprint)

International AI Safety Report 2025: First Key Update: Capabilities and Risk Implications

Stephen Clare

Carina Prunkl

Maksym Andriushchenko

BEN BUCKNALL

Philip Fox

Tiancheng Hu

Cameron Jones

Sam Manning

Nestor Maslej

Vasilios Mavroudis

Conor McGlynn

Malcolm Murray

Shalaleh Rismani

Charlotte Stix

Lucia Velasco

Nicole Wheeler

Daniel Privitera

Sören Mindermann

Daron Acemoglu … (see 36 more)

Thomas G. Dietterich

Fredrik Heintz

Geoffrey Hinton

Nick Jennings

Susan Leavy

Teresa Ludermir

Vidushi Marda

Helen Margetts

John McDermid

Jane Munga

Arvind Narayanan

Alondra Nelson

Clara Neppel

Sarvapali D. (Gopal) Ramchurn

Stuart Russell

Marietje Schaake

Bernhard Schölkopf

Alvaro Soto

Lee Tiedrich

Gael Varoquaux

Andrew Yao

Ya-Qin Zhang

Lambrini Das

Claire Dennis

Arianna Dini

Freya Hempleman

Samuel Kenny

Patrick King

Hannah Merchant

Jamie-Day Rawal

Rose Woolhouse

Jonathan Barry

Marc-Antoine Guérard

Claire Latendresse

Cassidy MacNeil

Benjamin Prud’homme

Since the publication of the first International AI Safety Report, AI capabilities have continued to improve across key domains. New trainin… (see more)g techniques that teach AI systems to reason step-by-step and inference-time enhancements have primarily driven these advances, rather than simply training larger models. As a result, general-purpose AI systems can solve more complex problems in a range of domains, from scientific research to software development. Their performance on benchmarks that measure performance in coding, mathematics, and answering expert-level science questions has continued to improve, though reliability challenges persist, with systems excelling on some tasks while failing completely on others. These capability improvements also have implications for multiple risks, including risks from biological weapons and cyber attacks. Finally, they pose new challenges for monitoring and controllability. This update examines how AI capabilities have improved since the first Report, then focuses on key risk areas where substantial new evidence warrants updated assessments.

2025-10-14

arXiv (preprint)

Learning What Matters: Steering Diffusion via Spectrally Anisotropic Forward Noise

Luca Scimeca

Thomas Jiralerspong

Berton Earnshaw

Jason Hartford

2025-10-06

ArXiv (preprint)

Monte Carlo Tree Diffusion for System 2 Planning

Jaesik Yoon

Hyeonseo Cho

Doojin Baek

Sungjin Ahn

Diffusion models have recently emerged as a powerful tool for planning. However, unlike Monte Carlo Tree Search (MCTS)-whose performance nat… (see more)urally improves with inference-time computation scaling-standard diffusion-based planners offer only limited avenues for the scalability. In this paper, we introduce Monte Carlo Tree Diffusion (MCTD), a novel framework that integrates the generative strength of diffusion models with the adaptive search capabilities of MCTS. Our method reconceptualizes denoising as a tree-structured process, allowing partially denoised plans to be iteratively evaluated, pruned, and refined. By selectively expanding promising trajectories while retaining the flexibility to revisit and improve suboptimal branches, MCTD achieves the benefits of MCTS such as controlling exploration-exploitation trade-offs within the diffusion framework. Empirical results on challenging long-horizon tasks show that MCTD outperforms diffusion baselines, yielding higher-quality solutions as inference-time computation increases.

2025-10-05

Proceedings of the 42nd International Conference on Machine Learning (published)

proceedings.mlr.press

Rejecting Hallucinated State Targets during Planning

Mingde Zhao

Tristan Sylvain

Romain Laroche

Doina Precup

In planning processes of computational decision-making agents, generative or predictive models are often used as "generators" to propose "ta… (see more)rgets" representing sets of expected or desirable states. Unfortunately, learned models inevitably hallucinate infeasible targets that can cause delusional behaviors and safety concerns. We first investigate the kinds of infeasible targets that generators can hallucinate. Then, we devise a strategy to identify and reject infeasible targets by learning a target feasibility evaluator. To ensure that the evaluator is robust and non-delusional, we adopted a design choice combining off-policy compatible learning rule, distributional architecture, and data augmentation based on hindsight relabeling. Attaching to a planning agent, the designed evaluator learns by observing the agent's interactions with the environment and the targets produced by its generator, without the need to change the agent or its generator. Our controlled experiments show significant reductions in delusional behaviors and performance improvements for various kinds of existing agents.

2025-10-05

Proceedings of the 42nd International Conference on Machine Learning (published)

proceedings.mlr.press

Towards a Formal Theory of Representational Compositionality

2025-10-05

Proceedings of the 42nd International Conference on Machine Learning (published)

proceedings.mlr.press

HVAC-SPICE: Value-Uncertainty In-Context RL with Thompson Sampling for Zero-Shot HVAC Control

Anaïs Berkes

Urban buildings consume 40\% of global energy, yet most rely on inefficient rule-based HVAC systems due to the impracticality of deploying a… (see more)dvanced controllers across diverse building stock. In-context reinforcement learning (ICRL) offers promise for rapid deployment without per-building training, but standard supervised learning objectives that maximise likelihood of training actions inherit behaviour-policy bias and provide weak exploration under the distribution shifts common when transferring across buildings and climates. We present SPICE (Sampling Policies In-Context with Ensemble uncertainty), a novel ICRL method specifically designed for zero-shot building control that addresses these fundamental limitations. SPICE introduces two key methodological innovations: (i) a propensity-corrected, return-aware training objective that prioritises high-advantage, high-uncertainty actions to enable improvement beyond suboptimal training demonstrations, and (ii) lightweight value ensembles with randomised priors that provide explicit uncertainty estimates for principled episode-level Thompson sampling. At deployment, SPICE samples one value head per episode and acts greedily, resulting in temporally coherent exploration without test-time gradients or building-specific models. We establish a comprehensive experimental protocol using the HOT dataset to evaluate SPICE across diverse building types and climate zones, focusing on the energy efficiency, occupant comfort, and zero-shot transfer capabilities that are critical for urban-scale deployment.

2025-09-29

NeurIPS.cc/2025/Workshop/UrbanAI (poster)

openreview.net

Recursive Self-Aggregation Unlocks Deep Thinking in Large Language Models

Johan Samir Obando Ceron

Brian R. Bartoldson

Bhavya Kailkhura

Guillaume Lajoie

Glen Berseth

Nikolay Malkin

Moksh Jain

Test-time scaling methods improve the capabilities of large language models (LLMs) by increasing the amount of compute used during inference… (see more) to make a prediction. Inference-time compute can be scaled in parallel by choosing among multiple independent solutions or sequentially through self-refinement. We propose Recursive Self-Aggregation (RSA), a test-time scaling method inspired by evolutionary methods that combines the benefits of both parallel and sequential scaling. Each step of RSA refines a population of candidate reasoning chains through aggregation of subsets to yield a population of improved solutions, which are then used as the candidate pool for the next iteration. RSA exploits the rich information embedded in the reasoning chains -- not just the final answers -- and enables bootstrapping from partially correct intermediate steps within different chains of thought. Empirically, RSA delivers substantial performance gains with increasing compute budgets across diverse tasks, model families and sizes. Notably, RSA enables Qwen3-4B-Instruct-2507 to achieve competitive performance with larger reasoning models, including DeepSeek-R1 and o3-mini (high), while outperforming purely parallel and sequential scaling strategies across AIME-25, HMMT-25, Reasoning Gym, LiveCodeBench-v6, and SuperGPQA. We further demonstrate that training the model to combine solutions via a novel aggregation-aware reinforcement learning approach yields significant performance gains. Code available at https://github.com/HyperPotatoNeo/RSA.

2025-09-29

ArXiv (preprint)

Active Attacks: Red-teaming LLMs via Adaptive Environments

Taeyoung YUN

Pierre-Luc St-Charles

Jinkyoo Park