Publications

Envisioning digital health ecosystem transformation in Canada «a conceptual foundation en Neuf Etapes.»
Nitika Pant Pai
Samira Abbasgholizadeh Rahimi
Juhi Tulsi
Susan Bartlett
Steven Grover
Ervin Sejdic
Canada’s journey towards digital health transformation is in a phase that precedes widespread catalytic change, trailing peer nations. In … (voir plus)this perspective piece, we discuss the nine steps that are key to catalyzing digital health transformation. We highlight the importance of foundational investments for health systems redesign. These investments in interoperability, unified digital core and ID, scalable health data systems, will enable precision-focused clinical care and prevention-focused public health with Smart Care Everywhere models. Our conceptual foundation highlights the importance of an agile health system with a unified digital core, capable of integrating multiple AI-enhanced digital tools, and managing the data deluge of multimodal data. We hereby advocate for a Smart, Scalable, Digitized, “Care Everywhere” model that can expand health care access to all of its populations: the served and the underserved. An essential component of the foundation is the creation of agile health systems and business models that prevent provider burnout while promoting collaborative, connected care that reaches served and under-served populations, with caring, compassion, enabling an improved engagement and connection. We also call for an investment in the continuous training of healthcare professionals, data professionals, and for an ethical, efficient implementation of AI/digital solutions everywhere from hospitals to community care settings. We also highlight the necessity of data governance policies to safeguard patient autonomy, promote data ownership, to ensure health data privacy, security, and confidentiality. This nine-step approach offers a framework for a unified, connected, patient-centred health ecosystem operationalized/made efficient with digital/AI solutions for patient communities, enabled by connectivity, caring, and compassion. Together, these nine steps serve as a conceptual foundation to enable a sustainable health system that advances access, equity, and efficiency in caring in health care nationwide.
2025-12-31
FACETS (publié)
doi.org
Escaping Policy Contraction: Contraction-Aware PPO (CaPPO) for Stable Language Model Fine-Tuning
Dun Yuan
Di Wu
Xue Liu
Reinforcement learning from human feedback (RLHF) with proximal policy optimization (PPO) is widely used but often yields less diverse outpu… (voir plus)ts than supervised fine-tuning, suggesting an effect in which the policy’s support contracts during on-policy optimization. We formalize this “policy contraction” with the Support Retention Ratio (SRR)—the share of SFT completions that retain non-negligible probability under the RL policy—and additionally track token-entropy, Kullback–Leibler (KL) divergence to the reference, and repetition. We propose Contraction-Aware PPO (CaPPO), a minimum-norm multi-gradient update that co-optimizes reward, entropy, and KL, paired with a controller that steers exploration toward a target token entropy. On HH-RLHF, Summarize-from-Feedback, and UltraFeedback with Qwen2-7B, Qwen2.5-14B, Mistral-7B-Instruct, and Llama-3-8B-Instruct, CaPPO increases win rate by 2 to 4 points over PPO and improves diversity, gaining 0.2 to 0.3 higher SRR. The gains persist under decoding sweeps and are robust to reward scaling and critic variance. Treating reward, diversity, and stability as first-class objectives, CaPPO mitigates contraction without sacrificing alignment performance.
2025-12-31
International Conference on Learning Representations (Accept (Poster))
openreview.net
openreview.net
Fast Proteome-Scale Protein Interaction Retrieval via Residue-Level Factorization
Jianan Zhao
Zhihao Zhan
Narendra Chaudhary
Xinyu Yuan
Zuobai Zhang
Qian Cong
Jian Zhou
Sanchit Misra
Jian Tang
Protein-protein interactions (PPIs) are mediated at the residue level. Most sequence-based PPI models consider residue-residue interactions … (voir plus)across two proteins, which can yield accurate interaction scores but are too slow to scale. At proteome scale, identifying candidate PPIs requires evaluating nearly *all possible protein pairs*. For
2025-12-31
International Conference on Learning Representations (Accept (Poster))
openreview.net
openreview.net
Fast Sphere Decoding of Short Systematic Polar-like Codes
Huayi Zhou
Y. Liu
Xiaosi Tan
Chen Ji
Warren J. Gross
Chuan Zhang
Short polar-like codes are competitive for low latency requirements in future communications. Systematic polar codes have not been shown to … (voir plus)offer substantial benefits for decoders beyond improving the bit error rate. In this paper, we demonstrate that the sparsity of the equivalent generator matrix of systematic polar codes significantly reduces calculation complexity when using sphere decoding (SD). We propose a fast SD (Fast-SD) for systematic polar codes. Numerical results indicate that the proposed Fast-SD reduces calculation complexity by up to 33.25% compared to SD on short high-rate codes while maintaining maximum likelihood performance.
2025-12-31
IEEE Transactions on Vehicular Technology (publié)
doi.org
FIN: Boosting binary code embedding by normalizing function inlinings
Mohammadhossein Amouei
Benjamin C. M. Fung
Philippe Charland
2025-12-31
Journal of Systems and Software (publié)
doi.org
Foci, waves, excitability: Self-organization of phase waves in a model of asymmetrically coupled embryonic oscillators
Anonymous
Paul François
Kaushik Roy
The segmentation clock is an emergent embryonic oscillator that controls the periodic formation of vertebrae precursors (or somites). It rel… (voir plus)ies on the self-organization at the presomitic mesoderm (PSM) level of multiple coupled cellular oscillators. Dissociation-reaggregation experiments have further revealed that ensembles made of such cellular oscillators self-organize into an oscillatory bidimensional system, showing concentric waves around multiple foci. Here, we systematically study the dynamics of a two-dimensional lattice of phase oscillators locally coupled to their nearest neighbors through a biharmonic coupling function of the form sinθ+Λsin^{2}θ. This coupling was inferred from the phase response curve of entrainment experiments on cell cultures, leading to the formulation of a minimal Elliptic Radial Isochron Cycle (ERIC) phase model. We show that such ERIC-based coupling parsimoniously explains the emergence of self-organized concentric phase wave patterns around multiple foci for a range of weak couplings and wide distributions of initial random phases, closely mimicking experimental conditions. We further study extended modalities of this problem to derive an atlas of possible behaviors. In particular, we predict the dominant observation of spirals over target wave patterns for initial phase distributions wider than approximately π. Since PSM cells further display properties of an excitable system, we also introduce excitability into our simple model and show that it also supports the observation of concentric phase waves for the conditions of the experiment. Our work suggests important modifications that can be made to the simple phase model with Kuramoto coupling, which can provide further layers of complexity and aid in the explanation of the spatial aspects of self-organization in the segmentation clock.
2025-12-31
Physical Review E (publié)
doi.org
Gait training combined with transcutaneous spinal stimulation to enhance lower limbs motor recovery in people with spinal cord injury: Pilot Study
Nicolas Hoang Quang
Marianne Cossette-Levasseur
Sammy-Jo Beauregard-Veillette
Nancy Dubé
El-Mehdi Meftah
Héloïse Bourgeois
Nok-Yeung Law
Amedeo Ceglia
Marco Bonizzato
Marina Martinez
Diana Zidarov
Dorothy Barthélemy
2025-12-31
Neuromodulation: Technology at the Neural Interface (publié)
doi.org
Generative Adversarial Post-Training Mitigates Reward Hacking in Live Human-AI Music Interaction
Yusong Wu
Stephen Brade
Aleksandra Teng Ma
Tia-Jane Fowler
Enning Yang
Berker Banar
Aaron Courville
Natasha Jaques
Cheng-Zhi Anna Huang
Most applications of generative AI involve a sequential interaction in which a person inputs a prompt and waits for a response, and where re… (voir plus)action time and adaptivity are not important factors. In contrast, live jamming is a collaborative interaction that requires real-time coordination and adaptation without access to the other player’s future moves, while preserving diversity to sustain a creative flow. Reinforcement learning post-training enables effective adaptation through on-policy interaction, yet it often reduces output diversity by exploiting coherence-based rewards. This collapse, known as ``reward hacking'', affects many RL post-training pipelines, but is especially harmful in live jamming, where musical creativity relies on dynamic variation and mutual responsiveness. In this paper, we propose a novel adversarial training method on policy-generated trajectories to mitigate reward hacking in RL post-training for melody-to-chord accompaniment. A co-evolving discriminator separates policy trajectories from the data distribution, while the policy maximizes the discriminator output in addition to coherence rewards to prevent collapse to trivial outputs. We evaluate accompaniment quality and output diversity in simulation with both fixed test melodies and learned melody agents, and we conduct a user study with the model deployed in a real-time interactive system with expert musicians. Quantitative evaluation and user feedback demonstrate improved output diversity, harmonic coherence, adaptation speed and user agency. Our results demonstrate a simple yet effective method to mitigate reward hacking in RL post-training of generative sequence models.
2025-12-31
International Conference on Learning Representations (Accept (Poster))
doi.org
openreview.net
Gistify: Codebase-Level Understanding via Runtime Execution
Hyunji Lee
Minseon Kim
Chinmay Singh
Matheus Pereira
Atharv Sonwane
Isadora White
Elias Stengel-Eskin
Mohit Bansal
Zhengxiang Shi
Alessandro Sordoni
Marc-Alexandre Côté
Eric Yuan
Lucas Caccia
As coding agents are increasingly deployed in large codebases, the need to automatically design challenging, codebase-level evaluation is ce… (voir plus)ntral. We propose Gistify, a task where a coding LLM must create a single, minimal, self-contained file that can reproduce a specific functionality of a codebase. The coding LLM is given full access to a codebase along with a specific entrypoint (e.g., a python command), and the generated file must replicate the output of the same command ran under the full codebase, while containing only the essential components necessary to execute the provided command. Success on Gistify requires both structural understanding of the codebase, accurate modeling of its execution flow as well as the ability to produce potentially large code patches. Our findings show that current state-of-the-art models struggle to reliably solve Gistify tasks, especially ones with long executions traces.
2025-12-31
International Conference on Learning Representations (Accept (Poster))
openreview.net
openreview.net
GraphOmni: A Comprehensive and Extensible Benchmark Framework for Large Language Models on Graph-theoretic Tasks
Hao Xu
Xiangru Jian
Xinjian Zhao
Wei Pang
Chao Zhang
Suyuchen Wang
Qixin Zhang
Zhengyuan Dong
Joao Monteiro
Bang Liu
Qiuzhuang Sun
Tianshu Yu
This paper introduces GraphOmni, a comprehensive benchmark designed to evaluate the reasoning capabilities of LLMs on graph-theoretic tasks … (voir plus)articulated in natural language. GraphOmni spans diverse graph types, serialization formats, and prompting schemes, substantially extending upon prior efforts in both scope and depth. Through systematic evaluation, we uncover critical interactions among these dimensions, revealing their decisive impact on model performance. Our experiments show that state-of-the-art closed-source models such as Claude-3.5 and o4-mini consistently lead overall, yet still leave considerable headroom, while open-source models display pronounced sensitivity to various design choices. Beyond the standard scope, larger graphs, real-world graphs, and additional NP-hard tasks are further discussed. We further analyze efficiency via output token usage, highlighting cost–accuracy trade-offs, and introduce a reinforcement learning-based optimizer that adaptively selects factor combinations, reducing evaluation cost by 75\% while retaining strong accuracy. This flexible and extensible benchmark not only deepens understanding of LLM performance on structured graph reasoning but also establishes a robust foundation for advancing model design and evaluation. The code and datasets are available at https://anonymous.4open.science/r/ID-14092.
2025-12-31
International Conference on Learning Representations (Accept (Poster))
doi.org
openreview.net
Grounding Computer Use Agents on Human Demonstrations
Aarash Feizi
Shravan Nayak
Xiangru Jian
Kevin Qinghong Lin
Kaixin Li
Rabiul Awal
Xing Han Lu
Johan Obando-Ceron
Juan A. Rodriguez
Nicolas Chapados
David Vázquez
Adriana Romero-Soriano
Reihaneh Rabbany
Perouz Taslakian
Christopher Pal
Spandana Gella
Sai Rajeswar
Building reliable computer-use agents requires grounding: accurately connecting natural language instructions to the correct on-screen eleme… (voir plus)nts. While large datasets exist for web and mobile interactions, high-quality resources for desktop environments are limited. To address this gap, we introduce GroundCUA, a large-scale desktop grounding dataset built from expert human demonstrations. It covers 87 applications across 12 categories and includes 56K screenshots, with every on-screen element carefully annotated for a total of over 3.56M human-verified annotations. From these demonstrations, we generate diverse instructions that capture a wide range of real-world tasks, providing high-quality data for model training. Using GroundCUA, we develop the GroundNext family of models that map instructions to their target UI elements. At both 3B and 7B scales, GroundNext achieves state-of-the-art results across five benchmarks using supervised fine-tuning, while requiring less than one-tenth the training data of prior work. Reinforcement learning post-training further improves performance. These results demonstrate the critical role of high-quality, expert-driven datasets in advancing general-purpose computer-use agents.
2025-12-31
International Conference on Learning Representations (Accept (Poster))
doi.org
openreview.net
Heterogeneous Low-Bandwidth Pre-Training of LLMs
Yazan Obeidi
Amir Sarfi
Joel Lidin
Paul Janson
Eugene Belilovsky
Pre-training large language models (LLMs) increasingly requires distributed compute, yet bandwidth constraints make it difficult to scale be… (voir plus)yond well-provisioned datacenters-especially when model parallelism forces frequent, large inter-device communications. We study whether SparseLoCo, a low-communication data parallel method based on infrequent synchronization and sparse pseudo-gradient exchange, can be combined with low-bandwidth pipeline model parallelism via activation and activation-gradient compression. We introduce a heterogeneous distributed training framework where some participants host full replicas on high-bandwidth interconnects, while resource-limited participants are grouped to jointly instantiate a replica using pipeline parallelism with subspace-projected inter-stage communication. To make the recently introduced subspace pipeline compression compatible with SparseLoCo, we study a number of adaptations. Across large-scale language modeling experiments (178M-1B parameters) on standard pretraining corpora, we find that activation compression composes with SparseLoCo at modest cost, while selective (heterogeneous) compression consistently improves the loss-communication tradeoff relative to compressing all replicas-especially at aggressive compression ratios. These results suggest a practical path to incorporating low-bandwidth model parallelism and heterogeneous participants into LLM pre-training.
2025-12-31
arXiv (prépublication)
doi.org
openreview.net