Publications

Perpetua: Multi-Hypothesis Persistence Modeling for Semi-Static Environments
Miguel Saavedra-Ruiz
Samer B. Nashed
Charlie Gauthier
Liam Paull
Many robotic systems require extended deployments in complex, dynamic environments. In such deployments, parts of the environment may change… (voir plus) between subsequent robot observations. Most robotic mapping or environment modeling algorithms are incapable of representing dynamic features in a way that enables predicting their future state. Instead, they opt to filter certain state observations, either by removing them or some form of weighted averaging. This paper introduces Perpetua, a method for modeling the dynamics of semi-static features. Perpetua is able to: incorporate prior knowledge about the dynamics of the feature if it exists, track multiple hypotheses, and adapt over time to enable predicting of future feature states. Specifically, we chain together mixtures of"persistence"and"emergence"filters to model the probability that features will disappear or reappear in a formal Bayesian framework. The approach is an efficient, scalable, general, and robust method for estimating the states of features in an environment, both in the present as well as at arbitrary future times. Through experiments on simulated and real-world data, we find that Perpetua yields better accuracy than similar approaches while also being online adaptable and robust to missing observations.
2025-10-18
2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (publié)
doi.org
arxiv.org
Continuously Learning Bug Locations
Paulina Stevia Nouwou Mindom
Leuson Da Silva
Amin Nikanjam
Foutse Khomh
Automatically locating buggy changesets associated with bug reports is crucial in the software development process. Deep Learning (DL)-based… (voir plus) techniques show promising results by leveraging structural information from the code and learning links between changesets and bug reports. However, since source code associated with changesets evolves, the performance of such models tends to degrade over time due to concept drift. Aiming to address this challenge, in this paper, we evaluate the potential of using Continual Learning (CL) techniques in multiple sub-tasks setting for bug localization (each of which operates on either stationary or non-stationary data), comparing it against a bug localization technique that leverages the BERT model, a deep reinforcement learning-based technique that leverages the A2C algorithm, and a DL-based function-level interaction model for semantic bug localization. Additionally, we enhanced the CL techniques by using logistic regression to identify and integrate the most significant bug-inducing factors. Our empirical evaluation across seven widely used software projects shows that CL techniques perform better than DL-based techniques by up to 61% in terms of Mean Reciprocal Rank (MRR), 44% in terms of Mean Average Precision (MAP), 83% in terms of top@1, 56% in terms of top@5, and 66% in terms of top@10 metrics in non-stationary setting. Further, we show that the CL techniques we studied are effective at localizing changesets relevant to a bug report while being able to mitigate catastrophic forgetting across the studied tasks and require up to 5x less computational effort during training. Our findings demonstrate the potential of adopting CL for bug localization in non-stationary settings, and we hope it helps to improve bug localization activities in Software Engineering using CL techniques.
2025-10-17
ACM Transactions on Software Engineering and Methodology (publié)
doi.org
arxiv.org
Hierarchical Differentiable Fluid Simulation
Xiangyu Kong
Arnaud Schoentgen
Damien Rioux‐Lavoie
Paul G. Kry
Derek Nowrouzezahrai
Differentiable simulation is an emerging field that offers a powerful and flexible route to fluid control. In grid‐based settings, high me… (voir plus)mory consumption is a long‐standing bottleneck that constrains optimization resolution. We introduce a two‐step algorithm that significantly reduces memory usage: our method first optimizes for bulk forces at reduced resolution, then refines local details over sub‐domains while maintaining differentiability. In trading runtime for memory, it enables optimization at previously unattainable resolutions. We validate its effectiveness and memory savings on a series of fluid control problems.
2025-10-16
Computer Graphics Forum (publié)
doi.org
Improving autoformalization via cycle consistency and incremental type-checking using language-model probabilistic programs
Mauricio Barba da Costa
Fabian Zaiser
Katherine M. Collins
Romir Patel
Timothy J. O'Donnell
Alexander K. Lew
Joshua B. Tenenbaum
Vikash Mansinghka
Cameron Freer
2025-10-16
NeurIPS.cc/2025/Workshop/MATH-AI (poster)
openreview.net
openreview.net
Learning Heuristics for Transit Network Design and Improvement with Deep Reinforcement Learning
Andrew Holliday
Ahmed El-Geneidy
Gregory Dudek
2025-10-16
Transportmetrica B: Transport Dynamics (publié)
doi.org
arxiv.org
Tracking the Evolving Role of Artificial Intelligence in Implementation Science: Protocol for a Living Scoping Review of Applications, Evaluation Approaches and Outcomes
Guillaume Fontaine
Olivia Di Lalla
Susan Michie
Byron J. Powell
Vivian Welch
James Thomas
Jeffery Chan
Samira Abbasgholizadeh-Rahimi
France Légaré
Janna Hastings
Sylvie D. Lambert
Justin Presseau
Sharon E. Straus
Ian D. Graham
Ruopeng An
Daniel N. Elakpa
Meagan Mooney
Alenda Dwiadila Matra Putra
Rachael Laritz
Natalie Taylor
Background Artificial intelligence (AI) offers significant opportunities to improve the field of implementation science by supporting… (voir plus) key activities such as evidence synthesis, contextual analysis, and decision-making to promote the adoption and sustainability of evidence-based practices. This living scoping review aims to: (1) map applications of AI in implementation research and practice; (2) identify evaluation approaches, reported outcomes, and potential risks; and (3) synthesize reported research gaps and opportunities for advancing the use of AI in implementation science. Methods This scoping review will follow the Joanna Briggs Institute (JBI) methodology and the Cochrane guidance for living systematic reviews. A living scoping review is warranted to keep up with the rapid changes in AI and its growing use in implementation science. We will include empirical studies, systematic reviews, grey literature, and policy documents that describe or evaluate applications of AI to support implementation science across the steps of the Knowledge-to-Action (KTA) Model. AI methods and models of interest include machine learning, deep learning, natural language processing, large language models, and related technologies and approaches. A search strategy will be applied to bibliographic databases (MEDLINE, Embase, CINAHL, PsycINFO, IEEE Xplore, Web of Science), relevant journals, conference proceedings, and preprint servers. Two reviewers will independently screen studies and extract data on AI characteristics, specific implementation task according to the KTA Model, evaluation methods, outcome domains, risks, and research gaps. Extracted data will be analyzed descriptively and synthesized narratively using a mapping approach aligned with the KTA Model. Discussion This living review will consolidate the evidence base on how AI is applied across the spectrum of implementation science. It will inform researchers, policymakers, and practitioners seeking to harness AI to improve the adoption, scale-up, and sustainability of evidence-based interventions, while identifying areas for methodological advancement and risk mitigation. Review registration Open Science Framework, May 2025: https://doi.org/10.17605/OSF.IO/2Q5DV
2025-10-16
F1000Research (publié)
doi.org
Nested-ReFT: Efficient Reinforcement Learning for Large Language Model Fine-Tuning via Off-Policy Rollouts
Maxime Heuillet
Yufei Cui
Boxing Chen
Audrey Durand
Prasanna Parthasarathi
2025-10-15
NeurIPS.cc/2025/Workshop/ER (accepté)
doi.org
openreview.net
'Ohhh, he's the boss!': Unpacking Power Dynamics Among Developers, Designers, and End-Users in FLOSS Usability
Jazlyn Hellman
Itai Epstein
Jinghui Cheng
Jin L.C. Guo
Addressing usability in free, libre, and open-source software (FLOSS) is a challenging issue, particularly due to a long-existing ''by devel… (voir plus)oper, for developer'' mentality. Engaging designers and end-users to work with developers can help improve its usability, but unequal power dynamics among those stakeholder roles must be mitigated. To explore how the power of different FLOSS stakeholders manifests and can be mediated during collaboration, we conducted eight design workshops with different combinations of key FLOSS stakeholders (i.e., developers, designers, and end-users). Leveraging existing theories on Dimensions of Power, we revealed how participants navigate existing role-based power structures through resource utilization, knowledge gap management, and experience referencing. We also observed that participants exhibited diverse behaviors confirming and challenging the status quo of FLOSS usability. Overall, our results contribute to a comprehensive understanding of the power dynamics among FLOSS stakeholders, providing valuable insights into ways to balance their power to improve FLOSS usability. Our work also serves as an exemplar of using design workshops as a research method to study power dynamics during collaboration that are usually hidden in the field.
2025-10-15
Proceedings of the ACM on Human-Computer Interaction (publié)
doi.org
arxiv.org
Predicting the Subhalo Mass Functions in Simulations from Galaxy Images
Andreas Filipp
Tri Nguyen
Laurence Perreault-Levasseur
J. Rose
Chris Lovell
Nicolas Payot
Francisco Villaescusa-navarro
Yashar Hezaveh
2025-10-15
ArXiv (prépublication)
arxiv.org
arxiv.org
It Takes Two: Your GRPO Is Secretly DPO
Yihong Wu
Liheng Ma
Lei Ding
Muzhi Li
Xinyu Wang
Kejia Chen
Zhan Su
Zhanguang Zhang
Chenyang Huang
Yingxue Zhang
Mark J. Coates
Jian-Yun Nie
Group Relative Policy Optimization (GRPO) is a prominent reinforcement learning algorithm for post-training Large Language Models (LLMs). I… (voir plus)t is commonly believed that GRPO necessitates a large group size to ensure stable training via precise statistical estimation, which incurs substantial computational overhead. In this work, we challenge this assumption by reframing GRPO as a form of contrastive learning, which reveals a fundamental connection to Direct Preference Optimization (DPO). Motivated by DPO's empirical success, we investigate the minimal two-rollout case (2-GRPO)—a configuration previously deemed infeasible. We provide a rigorous theoretical analysis to validate 2-GRPO and demonstrate empirically that it achieves performance on par with 16-GRPO, despite using only
2025-10-15
NeurIPS.cc/2025/Workshop/ER (spotlight)
openreview.net
openreview.net
A Comprehensive Review of Transmission and Distribution Optimal Power Flow Problems for the Integration of Distributed Energy Resources
Samuel M. Muhindo
Hanane Dagdougui
Antoine Lesage-Landry
Hussein Suprême
This paper presents a comprehensive review of coordination methods for addressing large-scale transmission and distribution optimal power fl… (voir plus)ow (TDOPF) problems involving distributed energy resources. With distinct objectives, each transmission and distribution system operator (TSO/DSO) independently seeks to solve its own optimal power flow (OPF) instance. First, iterative methods are reviewed, in which the central OPF is solved recursively by decomposing the full problem into smaller, more manageable sub-problems or by replacing peripheral portions of the network within the central OPF with reduced equivalent grids. Generally, the convergence to an optimal solution of the full problem when all sub-OPFs are coordinated is not guaranteed as iterative methods repeat procedures until the changes in control variables of the central OPF are minimal. Second, sequential methods are reviewed, in which the central OPF is solved sequentially in a fixed, nonrepeating procedure by considering previous results. Achieving a fair balance between TSO and DSO interests in sequential methods might adversely affect the performance of a largescale central OPF. The advantages and the limitations of the two coordination methods are presented based on the operation mode of TSO-DSO network. Future research opportunities for coordination methods of TSO-DSO network are drawn using the Kron reduction method and mean-field games.
2025-10-14
2025 IEEE Electrical Power and Energy Conference (EPEC) (publié)
doi.org
Co-Producing AI: Toward an Augmented, Participatory Lifecycle
Rashid Mushkani
Hugo Berard
Toumadher Ammar
Cassandre Chatonnier
Shin Koseki
Despite efforts to mitigate the inherent risks and biases of artificial intelligence (AI) algorithms, these algorithms can disproportionatel… (voir plus)y impact culturally marginalized groups. A range of approaches has been proposed to address or reduce these risks, including the development of ethical guidelines and principles for responsible AI, as well as technical solutions that promote algorithmic fairness. Drawing on design justice, expansive learning theory, and recent empirical work on participatory AI, we argue that mitigating these harms requires a fundamental re-architecture of the AI production pipeline. This re-design should center co-production, diversity, equity, inclusion (DEI), and multidisciplinary collaboration. We introduce an augmented AI lifecycle consisting of five interconnected phases: co-framing, co-design, co-implementation, co-deployment, and co-maintenance. The lifecycle is informed by four multidisciplinary workshops and grounded in themes of distributed authority and iterative knowledge exchange. Finally, we relate the proposed lifecycle to several leading ethical frameworks and outline key research questions that remain for scaling participatory governance.
2025-10-14
Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (publié)
doi.org
arxiv.org