Guillaume Lajoie

rohan.banerjee@mila.quebec

Biographie

Guillaume Lajoie est professeur agrégé au Département de mathématiques et de statistiques (DMS) de l'Université de Montréal et membre académique principal de Mila – Institut québécois d’intelligence artificielle. Il est titulaire d'une chaire CIFAR (CCAI Canada) ainsi que d'une chaire de recherche du Canada (CRC) en calcul et interfaçage neuronaux.

Ses recherches sont positionnées à l'intersection de l'IA et des neurosciences où il développe des outils pour mieux comprendre les mécanismes d'intelligence communs aux systèmes biologiques et artificiels. Les contributions de son groupe de recherche vont des progrès des paradigmes d'apprentissage à plusieurs échelles pour les grands systèmes artificiels aux applications en neurotechnologie. Dr. Lajoie participe activement aux efforts de développement responsables de l'IA, cherchant à identifier les lignes directrices et les meilleures pratiques pour l'utilisation de l'IA dans la recherche et au-delà.

Étudiants actuels

Rohan Banerjee

Collaborateur·rice alumni - Polytechnique

Doctorat - UdeM

Co-superviseur⋅e :

Hugo Larochelle

sangnie.bhardwaj@mila.quebec

Colin Bredenberg

Postdoctorat - UdeM

Co-superviseur⋅e :

colin.bredenberg@mila.quebec

Leo Choiniere

Doctorat - UdeM

leo.choiniere@mila.quebec

Claudia Clopath

Visiteur de recherche indépendant - University College London

claudia.clopath@mila.quebec

Eric Elmoznino

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Yoshua Bengio

eric.elmoznino@mila.quebec

Leo Gagnon

Doctorat - UdeM

leo.gagnon@mila.quebec

Tom George

Postdoctorat - McGill

Superviseur⋅e principal⋅e :

tomgeorge1@btinternet.com

Juan Guerra

Maîtrise recherche - Polytechnique

Superviseur⋅e principal⋅e :

Marco Bonizzato

juan.guerra@mila.quebec

thomas.jiralerspong@mila.quebec

Nanda Harishankar Krishna

Doctorat - UdeM

Anna Jahn

Visiteur de recherche indépendant - McGill

anna.jahn1@mila.quebec

Chen Jiang

Doctorat - McGill

Superviseur⋅e principal⋅e :

Paul Masset

chen.jiang@mila.quebec

Thomas Jiralerspong

Doctorat - UdeM

Co-superviseur⋅e :

Yoshua Bengio

Maîtrise recherche - UdeM

Co-superviseur⋅e :

Dhanya Sridhar

tejaskasetty@gmail.com

Doctorat - McGill

Superviseur⋅e principal⋅e :

pingsheng.li@mila.quebec

mathys.loiselle@mila.quebec

Mathys Loiselle

Stagiaire de recherche - Concordia

Co-superviseur⋅e :

Matt Perich

Ximeng Mao

Doctorat - UdeM

Co-superviseur⋅e :

Joelle Pineau

ximeng.mao@mila.quebec

Abdel Mfougouon Njupoun

Doctorat - UdeM

Co-superviseur⋅e :

alexandre.payeur@mila.quebec

abdelnjupoun@gmail.com

Doctorat - UdeM

Co-superviseur⋅e :

Visiteur de recherche indépendant - UdeM

Julia Price

Maîtrise recherche - UdeM

julia.price@mila.quebec

Mauricio Rivera

Maîtrise recherche - UdeM

Superviseur⋅e principal⋅e :

Marco Bonizzato

mauriv2010@gmail.com

hee-woon.ryoo@mila.quebec

Avery Ryoo

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Matt Perich

Doctorat - UdeM

Co-superviseur⋅e :

Lune Bellec

sainathp@mila.quebec

ezekiel.williams@mila.quebec

Ryan Vogt

Postdoctorat - UdeM

ryan.vogt@mila.quebec

Ezekiel Williams

Doctorat - UdeM

Visiteur de recherche indépendant - University of Western Ontario

jinkang.xiang@mila.quebec

Billets de blogue

Représentation graphique d'un nerf vague

21 mai 2025

Apprentissage automatique pour la segmentation des différentes activations des fibres nerveuses à partir des signaux neuronaux du cerveau vers le corps

par

Param Raval

Olivier Tessier-Larivière

Pascal Fortier-Poisson

Blake Richards

Guillaume Lajoie

Lire l'article

13 juin 2024

Que nous apprennent les distributions des coefficients synaptiques au sujet de l’apprentissage dans le cerveau ?

par

Roman Pogodin

Jonathan Cornford

Arna Ghosh

Gauthier Gidel

Guillaume Lajoie

Blake Richards

Lire l'article

Publications

A game theory for foundation models shows new paths to rational cooperation through similarity inference

Alexander Meulemans

Maciej Wołczyk

Marissa A. Weis

Rajai Nasser

Roberta Rocca

Seijin Kobayashi

Angelika Steger

Marcus Hutter

James Manyika

Rif A. Saurous

João Sacramento

Blaise Agüera y Arcas

As autonomous agents powered by foundation models are increasingly integrated into social and economic systems, understanding the principles… (voir plus) governing their collective behavior is essential for ensuring safety and cooperation. Classical game theory, the dominant framework for modeling rational interaction, is built upon the assumption of `decoupled agency,'where agents treat their own decision-making as independent of the environment and other actors. Modern AI agents, however, jointly predict their own future actions alongside external observations. Here, we report a striking finding: when interacting in stylized social dilemmas, foundation model agents engaging in optimal planning consistently converge to stable cooperation, directly contradicting classical game-theoretic predictions of mutual defection. To understand this phenomenon, we introduce the `embedded Bayesian agent,'a theoretical model for foundation model agents. By shifting from decoupled to embedded agency, these agents model themselves as part of the universe they inhabit, maintaining epistemic uncertainty about their own decision-making algorithms. We show that by inferring whether others are behaviorally similar, an embedded agent treats its own deliberation during planning as evidence: a decision to cooperate predicts a similar decision by a similar partner. We formalize this mechanism of similarity inference through the `embedded equilibrium,'a novel solution concept replacing the Nash equilibrium to provide a foundational game theory for the social behavior of modern AI agents.

2026-08-03

arXiv (prépublication)

Recursive Vision Language Models for General Symbolic Reasoning

Omid Nejati Manzari

Hassan Rivaz

Hard symbolic-reasoning tasks such as Sudoku, maze pathfinding, and ARC remain challenging for LLMs due to their fixed-depth autoregressive … (voir plus)reasoning, which limits systematic search, refinement, and backtracking. While recursive models such as Hierarchical Reasoning Model (HRM) and Tiny Recursive Model (TRM) address this limitation through iterative latent-state refinement, they are typically task-specific and do not leverage pretrained language priors. We propose R-Qwen, a recursive reasoning framework built upon a pretrained Qwen backbone. R-Qwen repeatedly refines a candidate solution through programmatic self-recursion and deep supervision, combining the structured iterative computation of recursive models with the linguistic and reasoning priors of pretrained LLMs. We further adapt Hierarchical Supervision Weighting (HSW) to autoregressive models by exponentially weighting losses across recursive steps. HSW reduces gradient variance by at least 50\%, improves the signal-to-noise ratio of stochastic gradients, and accelerates convergence. Across eight challenging benchmarks, R-Qwen consistently outperforms prior recursive reasoning models and substantially larger LLMs while using a comparable number of trainable parameters. Notably, on ARC-AGI dataset, our model achieves a 27.6\% improvement over the baseline, highlighting the effectiveness of recursive refinement for general symbolic reasoning. These results suggest that recursive reasoning mechanisms and pretrained language model priors are complementary approaches for improving symbolic puzzle-solving. Code and models will be released after acceptance.

2026-08-01

arXiv (prépublication)

Leveraging unlabelled data for generalizable neural population decoding

Ximeng Mao

Nanda H. Krishna

Avery Hee-Woon Ryoo

Matthew G. Perich

Robust and accurate neural decoders are integral to neurotechnologies such as brain-computer interfaces and closed-loop experiments. Recent … (voir plus)work has shown that tokenizing neural data at the spike level facilitates multi-session pretraining and delivers state-of-the-art decoding performance. However, current spike-based models are restricted to supervised learning (SL), limiting training to datasets with paired behavioural labels. To address this limitation, we introduce MOJO (Masked autOencoder-based JOint training), a training framework for spike-tokenizing models that jointly leverages self-supervised learning (SSL) via masked autoencoding and SL objectives. We evaluate MOJO on three spiking datasets spanning monkey motor cortex during reaching tasks and multi-regional mouse recordings during vision and decision making tasks, demonstrating superior performance over purely SL-trained models. This improvement is especially pronounced when training with limited labelled data, particularly in few-shot finetuning, where only a small amount of labelled data from a new session is available. Incorporating SSL also yields more interpretable neuronal representations, improving performance on brain region classification and spike-statistics prediction without explicit optimization for these tasks. We further show that MOJO generalizes beyond spiking data to human electrocorticography during speech, where it continues to outperform purely SL-trained models and achieves performance comparable to neuro-foundation models (NFMs) designed specifically for continuous signals. Overall, augmenting spike-tokenizing models with SSL improves performance in label-impoverished settings and enables the use of unlabelled data across various tasks and species, while generalizing to other neural modalities. These results suggest a path towards more flexible and scalable data usage when training NFMs.

2026-07-14

arXiv (prépublication)

Can In-Context Learning Support Intrinsic Curiosity?

Eric Elmoznino

Sangnie Bhardwaj

Johannes Von Oswald

Rajai Nasser

Blaise Agüera y Arcas

João Sacramento

Rif A. Saurous

Effective machine learning depends not only on how we model data, but also on what data we choose to collect. While large sequence models ha… (voir plus)ve revolutionized data modeling, the problem of automated data selection, or"intrinsic curiosity", remains a significant challenge. Classic approaches incentivize exploration by rewarding an agent based on its"learning progress", which measures how much a newly acquired observation improves a world model's predictive ability. However, evaluating these rewards traditionally requires expensive inner loops of gradient descent updates within each trajectory, rendering them computationally impractical at scale. In this work, we investigate whether the emergent in-context learning (ICL) capabilities of sequence models can eliminate this bottleneck by serving as immediate, update-free world models. Specifically, we evaluate whether an exploration policy can be trained to maximize learning progress, using solely the prediction errors and counterfactual context manipulations of an in-context learner. We first prove that in general Markov decision processes, this is in fact impossible in an unbiased way: the resulting intrinsic rewards either suffer from nuisance terms that bias their estimation of true learning progress, or they cannot be implemented using an in-context learner's prediction errors. Conversely, we prove a positive result for a broad subclass of non-temporal settings, encompassing active learning and Bayesian Experimental Design: here, ICL-derived rewards successfully bound and asymptotically converge to the true learning progress. We corroborate our theory with controlled experiments across continuous and symbolic environments, demonstrating that our ICL-driven framework successfully trains curious data-collection policies that explore optimally.

2026-06-16

arXiv (prépublication)

Simplifying the Modeling of Arbitrary Conditionals in Natural Language

Causal Transformers model sequences through an autoregressive factorization of the joint distribution, which enables efficient left-to-right… (voir plus) decoding and conditional likelihood computation. However, they cannot tractably sample from or evaluate arbitrary conditionals -- e.g., a block of text conditioned on past and future tokens. Recent work aims to solve this problem through novel architectures, but they often lead to sub-optimal modeling of such conditionals and degraded generations. We propose Arbitrary Conditionals GPT (AC-GPT) which introduces a simple modification to standard causal Transformers to enable evaluating and sampling from arbitrary conditionals -- including past, future, and mixed contexts -- within a single forward pass. Unlike prior approaches, our method preserves the standard left-to-right ordering and next-token prediction objective essential for both strong performance and efficient training on natural language. Crucially, this compatibility allows existing LLMs to be fine-tuned for arbitrary conditioning. Our empirical results indicate that our method outperforms baselines on modeling arbitrary conditionals, without degrading standard left-to-right performance.

2026-06-11

arXiv (prépublication)

CIAware-Bench: Benchmarking Control Intervention Awareness Across Frontier LLMs

Joachim Schaeffer

Thomas Jiralerspong

Alexander Panfilov

Jonas Geiping

Yoshua Bengio

Roland S. Zimmermann

AI control protocols oversee untrusted models by monitoring their actions and modifying potentially unsafe steps, often using a trusted mode… (voir plus)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 (prépublication)

Human learning of noninvasive brain–computer interfaces via manifold geometry

Erica L. Busch

E. Chandra Fincke

Smita Krishnaswamy

Nicholas B. Turk‐Browne

2026-06-08

Nature Neuroscience (publié)

Compositional Flow Matching with Factored Velocity Fields

Avery Hee-Woon Ryoo

Dane Malenfant

Matthew G Perich

Conditional generative models can have difficulty generating attribute combinations absent from training, even when each individual factor i… (voir plus)s densely covered, otherwise known as a failure to compositionally generalize. We propose a factored conditional flow matching architecture that uses a shared base velocity augmented by per-factor heads, summed at the bottleneck. We show that on the Shapes3D and MPI3D-real datasets, the factored architecture matches or beats a parameter-matched monolithic baseline under three structured zero-shot holdout strengths over a two-attribute lattice, notably lowering heldout FID by

2026-05-29

SPIGM @ International Conference on Machine Learning (poster)

openreview.net

Dynamics and Representation Structure of Local Approximations to Gradient-Based Learning in Linear Recurrent Neural Networks

Ezekiel Williams

Alexandre Payeur

Biological and neuromorphic recurrent neural networks (RNNs) are subject to spatial and temporal locality constraints on the information tha… (voir plus)t can plausibly be used during learning. A common strategy to satisfy these constraints is to modify gradient descent by neglecting non-local terms to varying degrees, as in random feedback local online (RFLO) learning and truncated backpropagation through time (tBPTT). However, the learning dynamics of these algorithms, and how they compare with BPTT, remain poorly understood. We apply dynamical systems theory to data-aligned linear RNNs -- whose dynamics can be separated into orthogonal modes -- to compare stationary solutions, stability properties, and convergence rates, finding qualitatively distinct behaviour for RFLO versus BPTT and one-step tBPTT. We further observe that the solutions learned by RFLO are restricted to low-rank perturbations of initial parameters, a result which holds beyond the data-aligned setting. Our work provides analytical insight into how locality constraints shape learning dynamics, with implications for neuroscientific models of learning and alternative optimization approaches for RNNs.

2026-05-28

arXiv (prépublication)

openreview.net

Modeling the hallucinatory effects of classical psychedelics in terms of replay-dependent plasticity mechanisms

Classical psychedelics induce complex visual hallucinations in humans, generating percepts that are coherent at a low level, but which have … (voir plus)surreal, dream-like qualities at a high level. While there are many hypotheses as to how classical psychedelics could induce these effects, there are no concrete mechanistic models that capture the variety of observed effects in humans, while remaining consistent with the known pharmacological effects of classical psychedelics on neural circuits. In this work, we propose the ‘oneirogen hypothesis,’ which posits that the perceptual effects of classical psychedelics are a result of their pharmacological actions inducing neural activity states that truly are more similar to dream-like states. We simulate classical psychedelics’ effects via manipulating neural network models trained on perceptual tasks with the Wake-Sleep algorithm. This established machine learning algorithm leverages two activity phases: a perceptual phase (wake) where sensory inputs are encoded, and a generative phase (dream) where the network internally generates activity consistent with stimulus-evoked responses. We simulate the action of psychedelics by partially shifting the model to the ‘Sleep’ state, which entails a greater influence of top-down connections, in line with the impact of psychedelics on apical dendrites. The effects resulting from this manipulation capture a number of experimentally observed phenomena, including the emergence of hallucinations, increases in stimulus-conditioned variability, and large increases in synaptic plasticity. We further provide a number of testable predictions which could be used to validate or invalidate our oneirogen hypothesis.

2026-04-20

eLife (publié)

Beyond Distribution Sharpening: The Importance of Task Rewards

Sarthak Mittal

Leo Gagnon

Frontier models have demonstrated exceptional capabilities following the integration of task-reward-based reinforcement learning (RL) into t… (voir plus)heir training pipelines, enabling systems to evolve from pure reasoning models into sophisticated agents. However, debate persists regarding whether RL genuinely instills new skills within a base model or merely sharpens its existing distribution to elicit latent capabilities. To address this dichotomy, we present an explicit comparison between distribution sharpening and task-reward-based learning, utilizing RL as a tool to implement both paradigms. Our analysis reveals the inherent limitations of distribution sharpening, demonstrating from first principles how and why the optima can be unfavorable and the approach fundamentally unstable. Furthermore, our experiments using Llama-3.2-3B-Instruct, Qwen2.5-3B-Instruct and Qwen3-4B-Instruct-2507 on math datasets confirm that sharpening yields limited gains, whereas incorporating task-based reward signal can greatly help achieve robust performance improvements and stable learning.

2026-04-16

arXiv (prépublication)

A Compression Perspective on Simplicity Bias

Mizu Nishikawa-Toomey

Sarthak Mittal

Dhanya Sridhar

Deep neural networks exhibit a simplicity bias, a well-documented tendency to favor simple functions over complex ones. In this work, we cas… (voir plus)t new light on this phenomenon through the lens of the Minimum Description Length principle, formalizing supervised learning as a problem of optimal two-part lossless compression. Our theory explains how simplicity bias governs feature selection in neural networks through a fundamental trade-off between model complexity (the cost of describing the hypothesis) and predictive power (the cost of describing the data). Our framework predicts that as the amount of available training data increases, learners transition through qualitatively different features -- from simple spurious shortcuts to complex features -- only when the reduction in data encoding cost justifies the increased model complexity. Consequently, we identify distinct data regimes where increasing data promotes robustness by ruling out trivial shortcuts, and conversely, regimes where limiting data can act as a form of complexity-based regularization, preventing the learning of unreliable complex environmental cues. We validate our theory on a semi-synthetic benchmark showing that the feature selection of neural networks follows the same trajectory of solutions as optimal two-part compressors.

2026-03-25

arXiv (prépublication)