Publications

Prépublications

• Did I Faithfully Say What I Thought? Bridging the Gap Between Neural Activity and Self-Explanations in Large Language Models
  Milan Bhan, Jean-Noel Vittaut, Nicolas Chesneau, Sarath Chandar et Marie-Jeanne Lesot
  In ArXiv, 2025.
  #NLP
  [arXiv]

• 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 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, Marc Szafraniec, Kapil Krishnakumar, Yong Li, Xiaodong Ma, Sarath Chandar, Franziska Meier*, Yann LeCun*, Michael Rabbat* et Nicolas Ballas*
  Technical Report, 2025.
  #DL
  [website], [arXiv], [code], [huggingface], [blogpost]

• Boosting LLM Reasoning via Spontaneous Self-Correction
  Xutong Zhao, Tengyu Xu, Xuewei Wang, Zhengxing Chen, Di Jin, Liang Tan, Yen-Ting, Zishun Yu, Zhuokai Zhao, Yun He, Sinong Wang, Han Fang, Sarath Chandar et Chen Zhu
  In ArXiv, 2025.
  #NLP, #RL
  [arXiv]

• Steering Large Language Model Activations in Sparse Spaces
  Reza Bayat, Ali Rahimi-Kalahroudi, Mohammad Pezeshki, Sarath Chandar et Pascal Vincent
  In ArXiv, 2025.
  #NLP, #DL
  [arXiv]

• Torque-Aware Momentum
  Pranshu Malviya, Gonçalo Mordido, Aristide Baratin, Reza Babanezhad Harikandeh, Gintare Karolina Dziugaite, Razvan Pascanu et Sarath Chandar
  In ArXiv, 2024.
  #DL
  [arXiv]

• TRecViT: A Recurrent Video Transformer
  Viorica Pătrăucean, Xu Owen He, Joseph Heyward, Chuhan Zhang, Mehdi S. M. Sajjadi, George-Cristian Muraru, Artem Zholus, Mahdi Karami, Ross Goroshin, Yutian Chen, Simon Osindero, João Carreira et Razvan Pascanu
  In ArXiv, 2024.
  #DL
  [arXiv]

• Too Big to Fool: Resisting Deception in Language Models
  Mohammad Reza Samsami, Mats Leon Richter, Juan Rodriguez, Megh Thakkar, Sarath Chandar et Maxime Gasse
  In ArXiv, 2024.
  #NLP
  [arXiv]

• Unraveling the Complexity of Memory in RL Agents: An Approach for Classification and Evaluation
  Egor Cherepanov, Nikita Kachaev, Artem Zholus, Alexey K. Kovalev et Aleksandr I. Panov
  In ArXiv, 2024.
  #RL
  [arXiv]

• Interpretability Needs a New Paradigm
  Andreas Madsen, Himabindu Lakkaraju, Siva Reddy et Sarath Chandar
  In ArXiv, 2024.
  #NLP, #DL
  [arXiv]

• Protein Language Models: Is Scaling Necessary?
  Quentin Fournier, Robert M. Vernon, Almer van der Sloot, Benjamin Schulz, Sarath Chandar et Christopher James Langmead
  In bioRxiv, 2024.
  #DL, #Other
  [bioRxiv], [code]

• Interpretability in Action: Exploratory Analysis of VPT, a Minecraft Agent
  Karolis Jucys, George Adamopoulos, Mehrab Hamidi, Stephanie Milani, Mohammad Reza Samsami, Artem Zholus, Sonia Joseph, Blake Richards, Irina Rish et Özgür Şimşek
  Mechanistic Interpretability Workshop, ICML, 2024.
  #Other
  [arXiv]

• Segmentation of Multiple Sclerosis Lesions across Hospitals: Learn Continually or Train from Scratch?
  Naga Karthik Enamundram, Anne Kerbrat, Pierre Labauge, Tobias Granberg, Jason Talbott, Daniel S. Reich, Massimo Filippi, Rohit Bakshi, Virginie Callot, Sarath Chandar et Julien Cohen-Adad
  In ArXiv, 2022.
  [Medical Imaging meets NeurIPS Workshop, 2022]
  #DL
  [arXiv], [code]

• Feature diversity in self-supervised learning
  Pranshu Malviya* et Arjun Vaithilingam Sudhakar*
  Conference on Lifelong Learning Agents (CoLLAs) Workshop Track, 2022.
  #DL
  [arXiv]

• An Introduction to Lifelong Supervised Learning
  Shagun Sodhani, Mojtaba Farmazi, Sanket Vaibhav Mehta, Pranshu Malviya, Mohamed Abdelsalam, Janarthanan Rajendran et Sarath Chandar
  In ArXiv, 2022.
  #DL
  [arXiv]

• Maximum Reward Formulation In Reinforcement Learning
  Sai Krishna Gottipati, Yashaswi Pathak, Rohan Nuttall, Raviteja Chunduru, Ahmed Touati, Sriram Ganapathi Subramanian, Matthew E Taylor et Sarath Chandar
  In arXiv, 2020.
  #RL
  [arXiv]