Publications

Next-Token Prediction Should be Ambiguity-Sensitive : A Meta-Learing Perspective
Leo Gagnon
Eric Elmoznino
Sarthak Mittal
Tom Marty
Tejas Kasetty
Dhanya Sridhar
Guillaume Lajoie
2025-06-11
ICML.cc/2025/Workshop/ES-FoMo-III (published)
openreview.net
openreview.net
NovoMolGen: Rethinking Molecular Language Model Pretraining
Kamran Chitsaz
Roshan Balaji
Quentin Fournier
Nirav Pravinbhai Bhatt
Sarath Chandar
2025-06-11
ICML.cc/2025/Workshop/GenBio (poster)
openreview.net
openreview.net
Overcoming Long-Context Limitations of State-Space Models via Context-Dependent Sparse Attention
Zhihao Zhan
Jianan Zhao
Zhaocheng Zhu
Jian Tang
Efficient long-context modeling remains a critical challenge for natural language processing (NLP), as the time complexity of the predominan… (see more)t Transformer architecture scales quadratically with the sequence length. While state-space models (SSMs) offer alternative sub-quadratic solutions, they struggle to capture long-range dependencies effectively. In this work, we focus on analyzing and improving the long-context modeling capabilities of SSMs. We show that the widely used synthetic task, associative recall, which requires a model to recall a value associated with a single key without context, insufficiently represents the complexities of real-world long-context modeling. To address this limitation, we extend the associative recall to a novel synthetic task, \emph{joint recall}, which requires a model to recall the value associated with a key given in a specified context. Theoretically, we prove that SSMs do not have the expressiveness to solve multi-query joint recall in sub-quadratic time complexity. To resolve this issue, we propose a solution based on integrating SSMs with Context-Dependent Sparse Attention (CDSA), which has the expressiveness to solve multi-query joint recall with sub-quadratic computation. To bridge the gap between theoretical analysis and real-world applications, we propose locality-sensitive Hashing Attention with sparse Key Selection (HAX), which instantiates the theoretical solution and is further tailored to natural language domains. Extensive experiments on both synthetic and real-world long-context benchmarks show that HAX consistently outperforms SSM baselines and SSMs integrated with context-independent sparse attention (CISA).
2025-06-11
ICML.cc/2025/Workshop/ES-FoMo-III (published)
doi.org
openreview.net
Progressive Inference-Time Annealing of Diffusion Models for Sampling from Boltzmann Densities
Tara Akhound-Sadegh
Jungyoon Lee
Joey Bose
Valentin De Bortoli
Arnaud Doucet
Michael M. Bronstein
Dominique Beaini
Siamak Ravanbakhsh
Kirill Neklyudov
Alexander Tong
Sampling efficiently from a target unnormalized probability density remains a core challenge, with relevance across countless high-impact sc… (see more)ientific applications. A promising approach towards this challenge is the design of amortized samplers that borrow key ideas, such as probability path design, from state-of-the-art generative diffusion models. However, all existing diffusion-based samplers remain unable to draw samples from distributions at the scale of even simple molecular systems. In this paper, we propose Progressive Inference-Time Annealing (PITA), a novel framework to learn diffusion-based samplers that combines two complementary interpolation techniques: I.) Annealing of the Boltzmann distribution and II.) Diffusion smoothing. PITA trains a sequence of diffusion models from high to low temperatures by sequentially training each model at progressively higher temperatures, leveraging engineered easy access to samples of the temperature-annealed target density. In the subsequent step, PITA enables simulating the trained diffusion model to procure training samples at a lower temperature for the next diffusion model through inference-time annealing using a novel Feynman-Kac PDE combined with Sequential Monte Carlo. Empirically, PITA enables, for the first time, equilibrium sampling of N-body particle systems, Alanine Dipeptide, and tripeptides in Cartesian coordinates with dramatically lower energy function evaluations. Code available at: https://github.com/taraak/pita
2025-06-11
ICML.cc/2025/Workshop/GenBio (spotlight)
doi.org
openreview.net
Self-Predictive Representations for Combinatorial Generalization in Behavioral Cloning
Daniel Lawson
Adriana Hugessen
Charlotte Cloutier
Glen Berseth
Khimya Khetarpal
Behavioral cloning (BC) methods trained with supervised learning (SL) are an effective way to learn policies from human demonstrations in do… (see more)mains like robotics. Goal-conditioning these policies enables a single generalist policy to capture diverse behaviors contained within an offline dataset. While goal-conditioned behavior cloning (GCBC) methods can perform well on in-distribution training tasks, they do not necessarily generalize zero-shot to tasks that require conditioning on novel state-goal pairs, i.e. combinatorial generalization. In part, this limitation can be attributed to a lack of temporal consistency in the state representation learned by BC; if temporally related states are encoded to similar latent representations, then the out-of-distribution gap for novel state-goal pairs would be reduced. Hence, encouraging this temporal consistency in the representation space should facilitate combinatorial generalization. Successor representations, which encode the distribution of future states visited from the current state, nicely encapsulate this property. However, previous methods for learning successor representations have relied on contrastive samples, temporal-difference (TD) learning, or both. In this work, we propose a simple yet effective representation learning objective,
2025-06-11
ArXiv (preprint)
arxiv.org
arxiv.org
The 2025 PNPL Competition: Speech Detection and Phoneme Classification in the LibriBrain Dataset
Gilad Landau
Miran Ozdogan
Gereon Elvers
Francesco Mantegna
Pratik Somaiya
Dulhan Hansaja Jayalath
Luisa Kurth
Teyun Kwon
Brendan Shillingford
Greg Farquhar
Minqi Jiang
Karim Jerbi
Hamza Abdelhedi
Yorguin Mantilla Ramos
Caglar Gulcehre
M. Woolrich
Natalie Voets
Oiwi Parker Jones
The advance of speech decoding from non-invasive brain data holds the potential for profound societal impact. Among its most promising appli… (see more)cations is the restoration of communication to paralysed individuals affected by speech deficits such as dysarthria, without the need for high-risk surgical interventions. The ultimate aim of the 2025 PNPL competition is to produce the conditions for an"ImageNet moment"or breakthrough in non-invasive neural decoding, by harnessing the collective power of the machine learning community. To facilitate this vision we present the largest within-subject MEG dataset recorded to date (LibriBrain) together with a user-friendly Python library (pnpl) for easy data access and integration with deep learning frameworks. For the competition we define two foundational tasks (i.e. Speech Detection and Phoneme Classification from brain data), complete with standardised data splits and evaluation metrics, illustrative benchmark models, online tutorial code, a community discussion board, and public leaderboard for submissions. To promote accessibility and participation the competition features a Standard track that emphasises algorithmic innovation, as well as an Extended track that is expected to reward larger-scale computing, accelerating progress toward a non-invasive brain-computer interface for speech.
2025-06-11
ArXiv (preprint)
arxiv.org
arxiv.org
The 2025 PNPL Competition: Speech Detection and Phoneme Classification in the LibriBrain Dataset
Gilad Landau
Miran Ozdogan
Gereon Elvers
Francesco Mantegna
Pratik Somaiya
Dulhan Hansaja Jayalath
Luisa Kurth
Teyun Kwon
Brendan Shillingford
Greg Farquhar
Minqi Jiang
Karim Jerbi
Hamza Abdelhedi
Yorguin Mantilla Ramos
Caglar Gulcehre
M. Woolrich
Natalie Voets
Oiwi Parker Jones
The advance of speech decoding from non-invasive brain data holds the potential for profound societal impact. Among its most promising appli… (see more)cations is the restoration of communication to paralysed individuals affected by speech deficits such as dysarthria, without the need for high-risk surgical interventions. The ultimate aim of the 2025 PNPL competition is to produce the conditions for an"ImageNet moment"or breakthrough in non-invasive neural decoding, by harnessing the collective power of the machine learning community. To facilitate this vision we present the largest within-subject MEG dataset recorded to date (LibriBrain) together with a user-friendly Python library (pnpl) for easy data access and integration with deep learning frameworks. For the competition we define two foundational tasks (i.e. Speech Detection and Phoneme Classification from brain data), complete with standardised data splits and evaluation metrics, illustrative benchmark models, online tutorial code, a community discussion board, and public leaderboard for submissions. To promote accessibility and participation the competition features a Standard track that emphasises algorithmic innovation, as well as an Extended track that is expected to reward larger-scale computing, accelerating progress toward a non-invasive brain-computer interface for speech.
2025-06-11
ArXiv (preprint)
doi.org
arxiv.org
Torsional-GFN: a conditional conformation generator for small molecules
Alexandra Volokhova
Lena Nehale Ezzine
Piotr Gaiński
Luca Scimeca
Emmanuel Bengio
Prudencio Tossou
Yoshua Bengio
Alex Hernandez-Garcia
2025-06-11
ICML.cc/2025/Workshop/GenBio (poster)
doi.org
openreview.net
V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
Mahmoud Assran
Adrien Bardes
David Fan
Quentin Garrido
Russell Howes
Mojtaba Komeili
Matthew J. Muckley
Ammar Rizvi
Claire Roberts
Koustuv Sinha
Artem Zholus
Sergio Arnaud
Abha Gejji
Ada Martin
Francois Robert Hogan
Daniel Dugas
Piotr Bojanowski
Vasil Khalidov
Patrick Labatut
Francisco Massa … (see 13 more)
Marc Szafraniec
K. Krishnakumar
Yong Li
Xiaodong Ma
Sarath Chandar
Franziska Meier
Yann LeCun
Michael Rabbat
Nicolas Ballas
Fair at Meta
Mila - Québec
AI Institute
Polytechnique Montréal
A major challenge for modern AI is to learn to understand the world and learn to act largely by observation. This paper explores a self-supe… (see more)rvised approach that combines internet-scale video data with a small amount of interaction data (robot trajectories), to develop models capable of understanding, predicting, and planning in the physical world. We first pre-train an action-free joint-embedding-predictive architecture, V-JEPA 2, on a video and image dataset comprising over 1 million hours of internet video. V-JEPA 2 achieves strong performance on motion understanding (77.3 top-1 accuracy on Something-Something v2) and state-of-the-art performance on human action anticipation (39.7 recall-at-5 on Epic-Kitchens-100) surpassing previous task-specific models. Additionally, after aligning V-JEPA 2 with a large language model, we demonstrate state-of-the-art performance on multiple video question-answering tasks at the 8 billion parameter scale (e.g., 84.0 on PerceptionTest, 76.9 on TempCompass). Finally, we show how self-supervised learning can be applied to robotic planning tasks by post-training a latent action-conditioned world model, V-JEPA 2-AC, using less than 62 hours of unlabeled robot videos from the Droid dataset. We deploy V-JEPA 2-AC zero-shot on Franka arms in two different labs and enable picking and placing of objects using planning with image goals. Notably, this is achieved without collecting any data from the robots in these environments, and without any task-specific training or reward. This work demonstrates how self-supervised learning from web-scale data and a small amount of robot interaction data can yield a world model capable of planning in the physical world.
2025-06-11
ArXiv (preprint)
doi.org
arxiv.org
V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
Mahmoud Assran
Adrien Bardes
David Fan
Quentin Garrido
Russell Howes
Mojtaba Komeili
Matthew J. Muckley
Ammar Rizvi
Claire Roberts
Koustuv Sinha
Artem Zholus
Sergio Arnaud
Abha Gejji
Ada Martin
Francois Robert Hogan
Daniel Dugas
Piotr Bojanowski
Vasil Khalidov
Patrick Labatut
Francisco Massa … (see 13 more)
Marc Szafraniec
K. Krishnakumar
Yong Li
Xiaodong Ma
Sarath Chandar
Franziska Meier
Yann LeCun
Michael Rabbat
Nicolas Ballas
Fair at Meta
Mila - Québec
AI Institute
Polytechnique Montréal
A major challenge for modern AI is to learn to understand the world and learn to act largely by observation. This paper explores a self-supe… (see more)rvised approach that combines internet-scale video data with a small amount of interaction data (robot trajectories), to develop models capable of understanding, predicting, and planning in the physical world. We first pre-train an action-free joint-embedding-predictive architecture, V-JEPA 2, on a video and image dataset comprising over 1 million hours of internet video. V-JEPA 2 achieves strong performance on motion understanding (77.3 top-1 accuracy on Something-Something v2) and state-of-the-art performance on human action anticipation (39.7 recall-at-5 on Epic-Kitchens-100) surpassing previous task-specific models. Additionally, after aligning V-JEPA 2 with a large language model, we demonstrate state-of-the-art performance on multiple video question-answering tasks at the 8 billion parameter scale (e.g., 84.0 on PerceptionTest, 76.9 on TempCompass). Finally, we show how self-supervised learning can be applied to robotic planning tasks by post-training a latent action-conditioned world model, V-JEPA 2-AC, using less than 62 hours of unlabeled robot videos from the Droid dataset. We deploy V-JEPA 2-AC zero-shot on Franka arms in two different labs and enable picking and placing of objects using planning with image goals. Notably, this is achieved without collecting any data from the robots in these environments, and without any task-specific training or reward. This work demonstrates how self-supervised learning from web-scale data and a small amount of robot interaction data can yield a world model capable of planning in the physical world.
2025-06-11
ArXiv (preprint)
arxiv.org
arxiv.org
V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
Mahmoud Assran
Adrien Bardes
David Fan
Quentin Garrido
Russell Howes
Mojtaba Komeili
Matthew J. Muckley
Ammar Rizvi
Claire Roberts
Koustuv Sinha
Artem Zholus
Sergio Arnaud
Abha Gejji
Ada Martin
Francois Robert Hogan
Daniel Dugas
Piotr Bojanowski
Vasil Khalidov
Patrick Labatut
Francisco Massa … (see 13 more)
Marc Szafraniec
K. Krishnakumar
Ying Li
Xiaodong Ma
Sarath Chandar
Franziska Meier
Yann LeCun
Michael Rabbat
Nicolas Ballas
Fair at Meta
Mila - Québec
AI Institute
Polytechnique Montréal
A major challenge for modern AI is to learn to understand the world and learn to act largely by observation. This paper explores a self-supe… (see more)rvised approach that combines internet-scale video data with a small amount of interaction data (robot trajectories), to develop models capable of understanding, predicting, and planning in the physical world. We first pre-train an action-free joint-embedding-predictive architecture, V-JEPA 2, on a video and image dataset comprising over 1 million hours of internet video. V-JEPA 2 achieves strong performance on motion understanding (77.3 top-1 accuracy on Something-Something v2) and state-of-the-art performance on human action anticipation (39.7 recall-at-5 on Epic-Kitchens-100) surpassing previous task-specific models. Additionally, after aligning V-JEPA 2 with a large language model, we demonstrate state-of-the-art performance on multiple video question-answering tasks at the 8 billion parameter scale (e.g., 84.0 on PerceptionTest, 76.9 on TempCompass). Finally, we show how self-supervised learning can be applied to robotic planning tasks by post-training a latent action-conditioned world model, V-JEPA 2-AC, using less than 62 hours of unlabeled robot videos from the Droid dataset. We deploy V-JEPA 2-AC zero-shot on Franka arms in two different labs and enable picking and placing of objects using planning with image goals. Notably, this is achieved without collecting any data from the robots in these environments, and without any task-specific training or reward. This work demonstrates how self-supervised learning from web-scale data and a small amount of robot interaction data can yield a world model capable of planning in the physical world.
2025-06-11
ArXiv (preprint)
doi.org
arxiv.org
Assessing and Learning Alignment of Unimodal Vision and Language Models
Le Zhang
Qian Yang
Aishwarya Agrawal
How well are unimodal vision and language models aligned? Although prior work have approached answering this question, their assessment meth… (see more)ods do not directly translate to how these models are used in practical vision-language tasks. In this paper, we propose a direct assessment method, inspired by linear probing, to assess vision-language alignment. We identify that the degree of alignment of the SSL vision models depends on their SSL training objective, and we find that the clustering quality of SSL representations has a stronger impact on alignment performance than their linear separability. Next, we introduce Swift Alignment of Image and Language (SAIL), a efficient transfer learning framework that aligns pretrained unimodal vision and language models for downstream vision-language tasks. Since SAIL leverages the strengths of pretrained unimodal models, it requires significantly fewer (6%) paired image-text data for the multimodal alignment compared to models like CLIP which are trained from scratch. SAIL training only requires a single A100 GPU, 5 hours of training and can accommodate a batch size up to 32,768. SAIL achieves 73.4% zero-shot accuracy on ImageNet (vs. CLIP's 72.7%) and excels in zero-shot retrieval, complex reasoning, and semantic segmentation. Additionally, SAIL improves the language-compatibility of vision encoders that in turn enhance the performance of multimodal large language models. The entire codebase and model weights are open-source: https://lezhang7.github.io/sail.github.io/
2025-06-10
2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (published)
doi.org
arxiv.org