Publications

Building spatial world models from sparse transitional episodic memories
Zizhan He
Maxime Daigle
Pouya Bashivan
2025-05-19
ArXiv (prépublication)
arxiv.org
arxiv.org
Calm-Whisper: Reduce Whisper Hallucination On Non-Speech By Calming Crazy Heads Down
Yingzhi Wang
Anas Alhmoud
Saad Alsahly
Muhammad Alqurishi
Mirco Ravanelli
2025-05-19
ArXiv (prépublication)
arxiv.org
arxiv.org
Field-Level Comparison and Robustness Analysis of Cosmological N-Body Simulations
Adrian E. Bayer
Francisco Villaescusa-navarro
Sammy Nasser Sharief
Romain Teyssier
Lehman H. Garrison
Laurence Perreault-Levasseur
Greg L. Bryan
Marco Gatti
E. Visbal
2025-05-19
ArXiv (prépublication)
arxiv.org
arxiv.org
Field-Level Comparison and Robustness Analysis of Cosmological N-Body Simulations
Adrian E. Bayer
Francisco Villaescusa-navarro
Sammy Nasser Sharief
Romain Teyssier
Lehman H. Garrison
Laurence Perreault-Levasseur
Greg L. Bryan
Marco Gatti
E. Visbal
2025-05-19
ArXiv (prépublication)
doi.org
arxiv.org
Generalizable Imitation Learning Through Pre-Trained Representations
Wei-Di Chang
Francois Hogan
Scott Fujimoto
David Meger
Gregory Dudek
In this paper we leverage self-supervised vision transformer models and their emergent semantic abilities to improve the generalization abil… (voir plus)ities of imitation learning policies. We introduce BC-ViT, an imitation learning algorithm that leverages rich DINO pre-trained Visual Transformer (ViT) patch-level embeddings to obtain better generalization when learning through demonstrations. Our learner sees the world by clustering appearance features into semantic concepts, forming stable keypoints that generalize across a wide range of appearance variations and object types. We show that this representation enables generalized behaviour by evaluating imitation learning across a diverse dataset of object manipulation tasks. Our method, data and evaluation approach are made available to facilitate further study of generalization in Imitation Learners.
2025-05-19
2025 IEEE International Conference on Robotics and Automation (ICRA) (publié)
doi.org
arxiv.org
Half Search Space is All You Need
Pavel Rumiantsev
Mark Coates
2025-05-19
ArXiv (prépublication)
arxiv.org
arxiv.org
RobusTAD: reference panel based annotation of nested topologically associating domains
Yanlin Zhang
Rola Dali
Mathieu Blanchette
Topologically associating domains (TADs) are fundamental units of 3D genomes and play essential roles in gene regulation. Hi-C data suggests… (voir plus) a hierarchical organization of TADs. Accurately annotating nested TADs from Hi-C data remains challenging, both in terms of the precise identification of boundaries and the correct inference of hierarchies. While domain boundary is relatively well conserved across cells, few approaches have taken advantage of this fact. Here, we present RobusTAD to annotate TAD hierarchies. It incorporates additional Hi-C data to refine boundaries annotated from the study sample. RobusTAD outperforms existing tools at boundary and domain annotation across several benchmarking tasks. Supplementary Information The online version contains supplementary material available at 10.1186/s13059-025-03568-9.
2025-05-19
Genome Biology (publié)
doi.org
Topological mapping for traversability-aware long-range navigation in off-road terrain
Jean-François Tremblay
Julie Alhosh
Louis Petit
Faraz Lotfi
Lara Landauro
David Meger
Autonomous robots navigating in off-road terrain like forests open new opportunities for automation. While off-road navigation has been stud… (voir plus)ied, existing work often relies on clearly delineated pathways. We present a method allowing for long-range planning, exploration and low-level control in unknown off-trail forest terrain, using vision and GPS only. We represent outdoor terrain with a topological map, which is a set of panoramic snapshots connected with edges containing traversability information. A novel traversability analysis method is demonstrated, predicting the existence of a safe path towards a target in an image. Navigating between nodes is done using goal-conditioned behavior cloning, leveraging the power of a pretrained vision transformer. An exploration planner is presented, efficiently covering an unknown off-road area with unknown traversability using a frontiers-based approach. The approach is successfully deployed to autonomously explore two 400 meters squared forest sites unseen during training, in difficult conditions for navigation.
2025-05-19
2025 IEEE International Conference on Robotics and Automation (ICRA) (publié)
doi.org
arxiv.org
Beyond Scalar Rewards: An Axiomatic Framework for Lexicographic MDPs
Mehran Shakerinava
Siamak Ravanbakhsh
Adam M. Oberman
2025-05-17
ArXiv (prépublication)
arxiv.org
arxiv.org
FedWeight: mitigating covariate shift of federated learning on electronic health records data through patients re-weighting
Mike He Zhu
Jun Bai
Na Li
Xiaoxiao Li
Dianbo Liu
David Buckeridge
Yue Li
2025-05-17
NPJ Digital Medicine (publié)
doi.org
Latent Veracity Inference for Identifying Errors in Stepwise Reasoning
Minsu Kim
Jean-Pierre R. Falet
Oliver E. Richardson
Xiaoyin Chen
Moksh J. Jain
Sungjin Ahn
Sungsoo Ahn
Yoshua Bengio
Chain-of-Thought (CoT) reasoning has advanced the capabilities and transparency of language models (LMs); however, reasoning chains can cont… (voir plus)ain inaccurate statements that reduce performance and trustworthiness. To address this, we propose to augment each reasoning step in a CoT with a latent veracity (or correctness) variable. To efficiently explore this expanded space, we introduce Veracity Search (VS), a discrete search algorithm over veracity assignments. It performs otherwise intractable inference in the posterior distribution over latent veracity values by leveraging the LM's joint likelihood over veracity and the final answer as a proxy reward. This efficient inference-time verification method facilitates supervised fine-tuning of an Amortized Veracity Inference (AVI) machine by providing pseudo-labels for veracity. AVI generalizes VS, enabling accurate zero-shot veracity inference in novel contexts. Empirical results demonstrate that VS reliably identifies errors in logical (ProntoQA), mathematical (GSM8K), and commonsense (CommonsenseQA) reasoning benchmarks, with AVI achieving comparable zero-shot accuracy. Finally, we demonstrate the utility of latent veracity inference for providing feedback during self-correction and self-improvement.
2025-05-17
ArXiv (prépublication)
arxiv.org
arxiv.org
Search-Based Correction of Reasoning Chains for Language Models
Minsu Kim
Jean-Pierre R. Falet
Oliver E. Richardson
Xiaoyin Chen
Moksh J. Jain
Sungjin Ahn
Sungsoo Ahn
Yoshua Bengio
2025-05-17
ArXiv (prépublication)
arxiv.org
arxiv.org