Institution profile

University of Montreal

Academic institutionnorthamerica · ca
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Research library782linked papers
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Selected work

Representative Papers

torchgfn: A PyTorch GFlowNet library

May 24, 2023arXiv.org

Existing GFlowNet research lacks a unified, scalable PyTorch implementation framework, hindering the development of novel training objectives, integration with diverse environments, and reproducible benchmarking. To address this, we introduce the first modular, object-oriented open-source GFlowNet library built on PyTorch. Our method standardizes environment interfaces and sampler abstractions, enables plug-and-play loss functions—including trajectory balance (TB), detailed balance (DB), and unnormalized balance (UB)—and decouples state-space representation, action policies, and flow parameterizations to facilitate customization and composability. The framework successfully reproduces multiple state-of-the-art results across canonical benchmarks, substantially lowering the barrier for algorithm validation and extension. The codebase is publicly released and has been widely adopted by the research community.

6 citationsRead paper

Imagining and building wise machines: The centrality of AI metacognition

Nov 04, 2024arXiv.org

Current AI systems exhibit intelligence but lack human-like wisdom, primarily due to the absence of metacognitive capabilities—such as intellectual humility, perspective-taking, and contextual adaptability—resulting in insufficient robustness, explainability, human-AI collaboration, and goal alignment in novel environments. To address this, the paper formally introduces “AI metacognition” as the cornerstone of artificial wisdom and proposes the first computationally grounded metacognitive capability framework, shifting beyond conventional object-level performance optimization. Methodologically, it integrates cognitive science modeling, explainable AI, value alignment, and novel wisdom-oriented benchmark design. The contributions include: (1) a theoretical foundation for wise AI; (2) a multidimensional evaluation framework; and (3) a principled implementation pathway. This work lays the groundwork for developing next-generation AI systems that are safe, trustworthy, and adaptive.

4 citations1 influentialRead paper

Discrete Feynman-Kac Correctors

Jan 15, 2026

This work addresses the challenge of flexibly controlling the output distribution in discrete diffusion models during generation. The authors propose a training-free, inference-stage control framework based on sequential Monte Carlo (SMC) methods, integrating ideas from Feynman–Kac path integrals to dynamically correct the generative distribution of discrete masked diffusion models. The approach enables temperature annealing, multi-conditional marginal distribution product sampling, and guidance via external rewards. It demonstrates significant effectiveness across diverse tasks, including efficient sampling from Ising model Boltzmann distributions, improved code generation, amortized inference, and the design of high-reward protein sequences.

3 citations1 influentialRead paper

Sufficient conditions for offline reactivation in recurrent neural networks

May 22, 2025International Conference on Learning Representations

Whether noise-driven recurrent neural networks (RNNs) can autonomously replay task-evoked neural activity during input-free resting periods remains an open question—particularly whether task-optimized networks inherently possess offline reactivation capability. Method: We formulate the network dynamics via stochastic differential equations, establish Lyapunov stability conditions, and validate our theory numerically on spatial localization and head-direction estimation tasks. Contribution/Results: We derive the first rigorous mathematical sufficient condition for offline reactivation in RNNs. We prove that denoising dynamics—enabling faithful replay—naturally emerge from smooth stimulus encoding and change-driven optimization, without ad hoc mechanisms. Both theoretical analysis and numerical experiments demonstrate that networks satisfying these optimization principles spontaneously recapitulate online activity patterns during rest, achieving reactivation fidelity exceeding 92%. This reveals offline reactivation as an intrinsic, emergent property of optimally trained recurrent systems, bridging online computation and offline memory consolidation.

3 citationsRead paper

Tight Lower Bounds and Improved Convergence in Performative Prediction

Dec 04, 2024arXiv.org

In the performatively predictive setting, model deployment induces distributional shifts, hindering convergence to a performative stable point. To address this, we propose Affine Risk Minimization (ARM), an algorithm that explicitly models the predictive impact on data distributions by affinely combining historical model parameters—leveraging past training snapshots with learned weights. Theoretically, we establish the first unified convergence upper bound for both existing and new algorithms, proving its tightness; reveal that historical information can break the single-iteration lower-bound barrier; and introduce the first lower-bound analysis framework for ARM-style methods. Empirically, ARM achieves faster convergence and higher accuracy at the performative stable point across multiple benchmarks.

3 citationsRead paper
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