Publications

LLM-as-a-Judge: Toward World Models for Slate Recommendation Systems

Modeling user preferences across domains remains a key challenge in slate recommendation (i.e. recommending an ordered sequence of items) re… (voir plus)search. We investigate how Large Language Models (LLM) can effectively act as world models of user preferences through pairwise reasoning over slates. We conduct an empirical study involving several LLMs on three tasks spanning different datasets. Our results reveal relationships between task performance and properties of the preference function captured by LLMs, hinting towards areas for improvement and highlighting the potential of LLMs as world models in recommender systems.

2025-11-05

ArXiv (prépublication)

doi.org

arxiv.org

Medication-based mortality prediction in COPD using machine learning and conventional statistical methods.

Ana Paula Pena-Gralle

Amélie Forget

Yohann Chiu

Marc-André Legault

M. Beauchesne

Lucie Blais

2025-11-04

International Journal of Medical Informatics (publié)

doi.org

Oneiris: An AI-augmented Brain-Computer Interface for Exploring Personal and Collective Dreamscapes

Antoine Bellemare‐Pepin

Philipp Thölke

Karim Jerbi CoCo Lab

Suzanne Kite

2025-11-04

Proceedings of the Conference on Animation and Interactive Art (publié)

doi.org

RefAgent: A Multi-agent LLM-based Framework for Automatic Software Refactoring

Khouloud Oueslati

Maxime Lamothe

Foutse Khomh

2025-11-04

ArXiv (prépublication)

doi.org

arxiv.org

Why Less is More (Sometimes): A Theory of Data Curation

Elvis Dopgima Dohmatob

Mohammad Pezeshki

Reyhane Askari Hemmat

2025-11-04

ArXiv (prépublication)

doi.org

arxiv.org

DeLLMphi: A Multi-Turn Method for Multi-Agent Forecasting

Andrew Robert Williams

Martin Weiss

Victoria Feere

Nasim Rahaman

Hugo Larochelle

The Delphi method is a structured forecasting process that engages experts in iterative prediction and reflection. Each round, experts submi… (voir plus)t forecasts to a mediator, receive an aggregated and synthesized response highlighting key arguments, and update their forecasts based on collective insight. However, Delphi panels are labour intensive, slow and hard to reproduce, requiring diverse knowledgeable participants to engage periodically across weeks or months. To address these constraints, we propose **DeLLMphi**, a forecasting method that replaces human experts and mediators with LLMs. We show (i) that providing example superforecaster reasoning traces and predictions helps to elicit more accurate forecasts from LLM experts, (ii) that the mediator plays the crucial role of surfacing different lines of reasoning and points of disagreement, and (iii) that multiple rounds and experts lead to better forecasts, showing that multi-turn interaction is key to DeLLMphi.

2025-11-03

NeurIPS.cc/2025/Workshop/MTI-LLM (poster)

openreview.net

Longitudinal functional connectivity during rest and task is differentially related to Alzheimer's pathology and episodic memory in older adults

Larissa Fischer

Jenna N. Adams

Eóin N. Molloy

Jennifer Tremblay-Mercier

Jordana Remz

Alexa Pichet Binette

M. Natasha Rajah

Sylvia Villeneuve

Anne Maass

PREVENT-AD Research Group

Changes in functional connectivity (FC) strength involving the medial temporal lobe (MTL) and posteromedial cortex (PMC) are related to earl… (voir plus)y Alzheimer’s pathology and alterations in episodic memory performance in cognitively unimpaired older adults, but their dynamics remain unclear. We examined how longitudinal changes in FC involving MTL and PMC during resting-state, episodic memory encoding, and retrieval relate to subsequent amyloid- and tau-PET burden, longitudinal episodic memory performance, and the APOE4 genotype in 152 cognitively unimpaired older adults from the PREVENT-AD cohort. We found APOE4- and fMRI paradigm-dependent associations of change in FC strength with pathology burden and change in episodic memory performance. Decreasing FC over time, or “hypoconnectivity”, within PMC during rest in APOE4 carriers and during retrieval in APOE4 non-carriers was related to more amyloid and tau, respectively. Conversely, increasing FC over time, or “hyperconnectivity”, within MTL during encoding in APOE4 carriers and between MTL and PMC during retrieval independent of APOE4 status was related to more tau. Further, increasing FC between MTL and PMC during rest, unlike during encoding, was beneficial for episodic memory. Our study highlights that pathology-related episodic memory network changes manifest differently during rest and task and have differential implications for episodic memory trajectories. The online version contains supplementary material available at 10.1038/s41598-025-21596-0.

2025-11-03

Scientific Reports (publié)

doi.org

Simultaneous detection and estimation in olfactory sensing

Chen Jiang

Matthew Y. He

Venkatesh N. Murthy

Cengiz Pehlevan

Jacob A. Zavatone-Veth

Paul Masset

The mammalian olfactory system shows an exceptional ability for rapid and accurate decoding of both the identity and concentration of odoran… (voir plus)ts. Previous works have used the theory of compressed sensing to elucidate the algorithmic basis for this capability: decoding odor information from the responses of a restricted repertoire of receptors is possible because only a few relevant odorants are present in any given sensory scene. However, existing circuit models for olfactory decoding still cannot contend with the complexity of naturalistic olfactory scenes; they are limited to detection of a handful of odorants. Here, we propose a model for olfactory compressed sensing inspired by simultaneous localization and mapping algorithms in navigation: the set of odors that are present in a given scene, and the concentration of those present odors, are inferred separately. To enable rapid inference of odor presence in a biologically-plausible recurrent circuit, our model leverages the framework of Mirrored Langevin Dynamics, which gives a general recipe for sampling from constrained distributions using rate-based dynamics. This results in a recurrent circuit model that can accurately infer presence and concentration at scale and can be mapped onto the primary cell types of the olfactory bulb. This frame-work offers a path towards circuit models—for olfactory sensing and beyond—that both perform well in naturalistic environments and make experimentally-testable predictions for neural response dynamics.

2025-11-02

bioRxiv (prépublication)

doi.org

Brain-like neural dynamics for behavioral control develop through reinforcement learning

Olivier Codol

Nanda H. Krishna

Guillaume Lajoie

Matthew G. Perich

During development, neural circuits are shaped continuously as we learn to control our bodies. The ultimate goal of this process is to produ… (voir plus)ce neural dynamics that enable the rich repertoire of behaviors we perform. What begins as a series of “babbles” coalesces into skilled motor output as the brain rapidly learns to control the body. However, the nature of the teaching signal underlying this normative learning process remains elusive. Here, we test two well-established and biologically plausible theories—supervised learning (SL) and reinforcement learning (RL)—that could explain how neural circuits develop the capacity for skilled movements. We trained recurrent neural networks to control a biomechanical model of a primate arm using either SL or RL and compared the resulting neural dynamics to populations of neurons recorded from the motor cortex of monkeys performing the same movements. Intriguingly, only RL-trained networks produced neural activity that matched their biological counterparts in terms of both the geometry and dynamics of population activity. We show that this similarity with biological brains depends critically on matching biomechanical properties of the limb. Dynamical analysis on network activity revealed that our RL-trained networks operate at the “edge of chaos”, a dynamical regime known for its computational richness, greater memory capacity, and robust plasticity properties. We then demonstrated that monkeys and RL-trained networks, but not SL-trained networks, show a strikingly similar capacity for robust short-term behavioral adaptation to a movement perturbation, indicating a fundamental and general commonality in the neural control policy. Together, our results support the hypothesis that neural dynamics for behavioral control emerge through a process akin to reinforcement learning. The resulting neural circuits offer numerous advantages for adaptable behavioral control over simpler and more efficient learning rules and expand our understanding of how developmental processes shape neural dynamics.

2025-11-01

bioRxiv (prépublication)

doi.org

Survey on <scp>AI</scp> Ethics: A Socio‐Technical Perspective

Dave Mbiazi

Meghana Bhange

Maryam Babaei

Ivaxi Sheth

Patrik Joslin Kenfack

Samira Ebrahimi Kahou

Abstract The past decade has observed a significant advancement in AI, with deep learning‐based models being deployed in diverse scenarios… (voir plus), including safety‐critical applications. As these AI systems become deeply embedded in our societal infrastructure, the repercussions of their decisions and actions have significant consequences, making the ethical implications of AI deployment highly relevant and essential. The ethical concerns associated with AI are multifaceted, including challenging issues of fairness, privacy and data protection, responsibility and accountability, safety and robustness, transparency and explainability, and environmental impact. These principles together form the foundations of ethical AI considerations that concern every stakeholder in the AI system lifecycle. In light of the present ethical and future x‐risk concerns, governments have shown increasing interest in establishing guidelines for the ethical deployment of AI. This work unifies the current and future ethical concerns of deploying AI into society. While we acknowledge and appreciate the technical surveys for each of the ethical principles concerned, in this paper, we aim to provide a comprehensive overview that not only addresses each principle from a technical point of view but also discusses them from a social perspective.

2025-11-01

Computational Intelligence (publié)

doi.org

arxiv.org

AFRIDOC-MT: Document-level MT Corpus for African Languages

Jesujoba Oluwadara Alabi

Israel Abebe Azime

Miaoran Zhang

Cristina España-Bonet

Rachel Bawden

Dawei Zhu

David Ifeoluwa Adelani

Clement Odoje

Idris Akinade

Iffat Maab

Davis David

Shamsuddeen Hassan Muhammad

Neo Putini

David O. Ademuyiwa

Andrew Caines

Dietrich Klakow

This paper introduces AFRIDOC-MT, a document-level multi-parallel translation dataset covering English and five African languages: Amharic, … (voir plus)Hausa, Swahili, Yor\`ub\'a, and Zulu. The dataset comprises 334 health and 271 information technology news documents, all human-translated from English to these languages. We conduct document-level translation benchmark experiments by evaluating neural machine translation (NMT) models and large language models (LLMs) for translations between English and these languages, at both the sentence and pseudo-document levels. These outputs are realigned to form complete documents for evaluation. Our results indicate that NLLB-200 achieved the best average performance among the standard NMT models, while GPT-4o outperformed general-purpose LLMs. Fine-tuning selected models led to substantial performance gains, but models trained on sentences struggled to generalize effectively to longer documents. Furthermore, our analysis reveals that some LLMs exhibit issues such as under-generation, repetition of words or phrases, and off-target translations, especially for African languages.

2025-10-31

Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (publié)

doi.org

arxiv.org

CAVE: Detecting and Explaining Commonsense Anomalies in Visual Environments

Rishika Bhagwatkar

Syrielle Montariol

Angelika Romanou

Beatriz Borges

Irina Rish

Antoine Bosselut

2025-10-31