Publications

Spatio-Temporal Conditional Diffusion Models for Forecasting Future Multiple Sclerosis Lesion Masks Conditioned on Treatments
Gian Mario Favero
Ge Ya Luo
Nima Fathi
Justin Szeto
Douglas Arnold
Brennan Nichyporuk
Chris Pal
Tal Arbel
Image-based personalized medicine has the potential to transform healthcare, particularly for diseases that exhibit heterogeneous progressio… (voir plus)n such as Multiple Sclerosis (MS). In this work, we introduce the first treatment-aware spatio-temporal diffusion model that is able to generate future masks demonstrating lesion evolution in MS. Our voxel-space approach incorporates multi-modal patient data, including MRI and treatment information, to forecast new and enlarging T2 (NET2) lesion masks at a future time point. Extensive experiments on a multi-centre dataset of 2131 patient 3D MRIs from randomized clinical trials for relapsing-remitting MS demonstrate that our generative model is able to accurately predict NET2 lesion masks for patients across six different treatments. Moreover, we demonstrate our model has the potential for real-world clinical applications through downstream tasks such as future lesion count and location estimation, binary lesion activity classification, and generating counterfactual future NET2 masks for several treatments with different efficacies. This work highlights the potential of causal, image-based generative models as powerful tools for advancing data-driven prognostics in MS.
2025-08-09
ArXiv (prépublication)
doi.org
arxiv.org
Spatio-Temporal Conditional Diffusion Models for Forecasting Future Multiple Sclerosis Lesion Masks Conditioned on Treatments
Gian Mario Favero
Ge Ya Luo
Nima Fathi
Justin Szeto
Douglas Arnold
Brennan Nichyporuk
Chris Pal
Tal Arbel
2025-08-09
ArXiv (prépublication)
arxiv.org
arxiv.org
CISO: Species Distribution Modeling Conditioned on Incomplete Species Observations
Hager Radi
Mélisande Teng
Robin Zbinden
Laura Pollock
Hugo Larochelle
Devis Tuia
David Rolnick
Species distribution models (SDMs) are widely used to predict species'geographic distributions, serving as critical tools for ecological res… (voir plus)earch and conservation planning. Typically, SDMs relate species occurrences to environmental variables representing abiotic factors, such as temperature, precipitation, and soil properties. However, species distributions are also strongly influenced by biotic interactions with other species, which are often overlooked. While some methods partially address this limitation by incorporating biotic interactions, they often assume symmetrical pairwise relationships between species and require consistent co-occurrence data. In practice, species observations are sparse, and the availability of information about the presence or absence of other species varies significantly across locations. To address these challenges, we propose CISO, a deep learning-based method for species distribution modeling Conditioned on Incomplete Species Observations. CISO enables predictions to be conditioned on a flexible number of species observations alongside environmental variables, accommodating the variability and incompleteness of available biotic data. We demonstrate our approach using three datasets representing different species groups: sPlotOpen for plants, SatBird for birds, and a new dataset, SatButterfly, for butterflies. Our results show that including partial biotic information improves predictive performance on spatially separate test sets. When conditioned on a subset of species within the same dataset, CISO outperforms alternative methods in predicting the distribution of the remaining species. Furthermore, we show that combining observations from multiple datasets can improve performance. CISO is a promising ecological tool, capable of incorporating incomplete biotic information and identifying potential interactions between species from disparate taxa.
2025-08-08
ArXiv (prépublication)
arxiv.org
arxiv.org
Long Range Navigator (LRN): Extending robot planning horizons beyond metric maps
Matt Schmittle
Rohan Baijal
Nathan Hatch
Rosario Scalise
Mateo Guaman Castro
Sidharth Talia
Khimya Khetarpal
Byron Boots
Siddhartha Srinivasa
2025-08-08
robot-learning.org/CoRL/2025/Conference (poster)
doi.org
openreview.net
RoboArena: Distributed Real-World Evaluation of Generalist Robot Policies
Pranav Atreya
Karl Pertsch
Tony Lee
Moo Jin Kim
Arhan Jain
Artur Kuramshin
Cyrus Neary
Edward S. Hu
Kanav Arora
Kirsty Ellis
Luca Macesanu
Matthew Leonard
Meedeum Cho
Özgür Aslan
Shivin Dass
Tony Wang
Xingfang Yuan
Abhishek Gupta
Dinesh Jayaraman
Glen Berseth … (voir 6 de plus)
Kostas Daniilidis
Roberto Martín-Martín
Youngwoon Lee
Percy Liang
Chelsea Finn
Sergey Levine
2025-08-08
robot-learning.org/CoRL/2025/Conference (présentation orale)
openreview.net
openreview.net
Bias-inducing geometries: an exactly solvable data model with fairness implications
Stefano Sarao Mannelli
Federica Gerace
Negar Rostamzadeh
Luca Saglietti
2025-08-07
Physical Review E (publié)
doi.org
arxiv.org
Whither symbols in the era of advanced neural networks?
Thomas L. Griffiths
Brenden M. Lake
R. Thomas McCoy
Ellie Pavlick
Taylor Webb
Some of the strongest evidence that human minds should be thought about in terms of symbolic systems has been the way they combine ideas, pr… (voir plus)oduce novelty, and learn quickly. We argue that modern neural networks -- and the artificial intelligence systems built upon them -- exhibit similar abilities. This undermines the argument that the cognitive processes and representations used by human minds are symbolic, although the fact that these neural networks are typically trained on data generated by symbolic systems illustrates that such systems play an important role in characterizing the abstract problems that human minds have to solve. This argument leads us to offer a new agenda for research on the symbolic basis of human thought.
2025-08-07
ArXiv (prépublication)
doi.org
arxiv.org
Persistent Instability in LLM's Personality Measurements: Effects of Scale, Reasoning, and Conversation History
Tommaso Tosato
Saskia Helbling
Yorguin-Jose Mantilla-Ramos
Mahmood Hegazy
Alberto Tosato
D. Lemay
Irina Rish
Guillaume Dumas
2025-08-06
ArXiv (prépublication)
doi.org
arxiv.org
Persistent Instability in LLM's Personality Measurements: Effects of Scale, Reasoning, and Conversation History
Tommaso Tosato
Saskia Helbling
Yorguin-Jose Mantilla-Ramos
Mahmood Hegazy
Alberto Tosato
D. Lemay
Irina Rish
Guillaume Dumas
Large language models require consistent behavioral patterns for safe deployment, yet their personality-like traits remain poorly understood… (voir plus). We present PERSIST (PERsonality Stability in Synthetic Text), a comprehensive evaluation framework testing 25+ open-source models (1B-671B parameters) across 500,000+ responses. Using traditional (BFI-44, SD3) and novel LLM-adapted personality instruments, we systematically vary question order, paraphrasing, personas, and reasoning modes. Our findings challenge fundamental deployment assumptions: (1) Even 400B+ models exhibit substantial response variability (SD>0.4); (2) Minor prompt reordering alone shifts personality measurements by up to 20%; (3) Interventions expected to stabilize behavior, such as chain-of-thought reasoning, detailed personas instruction, inclusion of conversation history, can paradoxically increase variability; (4) LLM-adapted instruments show equal instability to human-centric versions, confirming architectural rather than translational limitations. This persistent instability across scales and mitigation strategies suggests current LLMs lack the foundations for genuine behavioral consistency. For safety-critical applications requiring predictable behavior, these findings indicate that personality-based alignment strategies may be fundamentally inadequate.
2025-08-06
ArXiv (prépublication)
arxiv.org
arxiv.org
Single-nucleus chromatin accessibility profiling identifies cell types and functional variants contributing to major depression.
Anjali Chawla
Doruk Cakmakci
Laura M. Fiori
Wenmin Zang
Malosree Maitra
Jennie Yang
Dariusz Żurawek
Gabriella Frosi
Reza Rahimian
Haruka Mitsuhashi
MA Davoli
Ryan Denniston
Gary Gang Chen
V. Yerko
Deborah Mash
Kiran Girdhar
S. Akbarian
Naguib Mechawar
Matthew Suderman
Yue Li … (voir 2 de plus)
Corina Nagy
Gustavo Turecki
2025-08-05
Nature Genetics (publié)
doi.org
Single-nucleus chromatin accessibility profiling identifies cell types and functional variants contributing to major depression
Anjali Chawla
Doruk Cakmakci
Laura M. Fiori
Wenmin Zang
Malosree Maitra
Jennie Yang
Dariusz Żurawek
Gabriella Frosi
Reza Rahimian
Haruka Mitsuhashi
Maria Antonietta Davoli
Ryan Denniston
Gary Gang Chen
Volodymyr Yerko
Deborah Mash
Kiran Girdhar
Schahram Akbarian
Naguib Mechawar
Matthew Suderman
Yue Li … (voir 2 de plus)
Corina Nagy
Gustavo Turecki
2025-08-05
Nature Genetics (publié)
doi.org
Single-nucleus chromatin accessibility profiling identifies cell types and functional variants contributing to major depression
Anjali Chawla
Doruk Cakmakci
Laura M. Fiori
Wenmin Zang
Malosree Maitra
Jennie Yang
Dariusz Żurawek
Gabriella Frosi
Reza Rahimian
Haruka Mitsuhashi
Maria Antonietta Davoli
MA Davoli
Ryan Denniston
Gary Gang Chen
Volodymyr Yerko
Deborah Mash
Kiran Girdhar
Schahram Akbarian
Naguib Mechawar
Matthew Suderman … (voir 3 de plus)
Yue Li
Corina Nagy
Gustavo Turecki
2025-08-05
Nature Genetics (publié)
doi.org