World Models Reading Group

The World Models Reading Group meets biweekly at Mila to discuss recent work on world models.

When
Thursday, 3:00–4:00 pm (ET)
Where
Mila, Auditorium 2 — and on Zoom
Updates
Join the mailing list or subscribe by email (no Google account needed) At Mila? Follow #world-models on Slack

Schedule

Upcoming

  • Thu, Oct 15, 2026 3:00 pm ET Next
    Hallucination in World Models is Predictable and Preventable Nicklas Hansen

    Abstract

    Generative world models now render strikingly realistic, action-controllable futures, yet they frequently hallucinate: rollouts remain visually fluent while drifting from the true dynamics. This raises two questions: can we tell when a world model is hallucinating, and what can we do about it? In this talk, I will address both, and argue that hallucination is, first and foremost, a data coverage problem. I will first introduce MMBench2, a 427-hour, 210-task dataset for visual world modeling with ground-truth actions, rewards, and live simulators, along with a 350M-parameter world model trained on this dataset. Using this testbed, I will show that hallucination is systematic, that each type of hallucination ties to specific model components, and that simple uncertainty metrics computed from the model’s own predictions reveal where it will fail at runtime. I will then show that the same metrics can be used for mitigation: coverage-aware sampling during training, and curiosity-driven data collection. I will conclude with open questions, including how these findings carry over to real-world data.

    Bio

    Nicklas Hansen is a postdoctoral researcher at NYU Courant working with Lerrel Pinto and Yann LeCun. Nick’s research focuses on developing generalist AI agents that learn from interaction. They recently completed their PhD at UC San Diego advised by Xiaolong Wang and Hao Su, and have also spent time at NVIDIA Research, Meta AI (FAIR), and UC Berkeley.
  • Thu, Oct 29, 2026 3:00 pm ET
    Title TBD David Klindt
  • Thu, Nov 12, 2026 3:00 pm ET
    Title TBD Artem Zholus
  • Thu, Nov 26, 2026 3:00 pm ET
    Title TBD Sherry Yang

Past

  • Thu, Oct 1, 2026 3:00 pm ET
    Towards World Models via Joint-Embedding Predictive Architectures Michael Rabbat

    Abstract

    This talk provides an overview and motivation for why world models are a necessary component of future AI systems. The term “world model” is overloaded in the broader discourse. For the purposes of this talk I’ll argue that a world model should: (a) construct an agent’s internal representation of the world through perception, and (b) should be able to predict the evolution of this representation through time, including in response to the agent’s actions. Such a world model enables planning. Two main research questions are then: how to learn such a world model from data? and how to use it for planning and control? I’ll give an overview of our work on Joint-Embedding Predictive Architectures (JEPAs) and will discuss interesting directions for future work.

    Bio

    Michael Rabbat is Co-founder and VP World Models at Advanced Machine Intelligence (AMI Labs), where he is building a new breed of AI systems focused on understanding and reasoning about the world. He is also an Associate Industry Member at Mila. Previously, Michael was a Research Director at Meta, where he was a founding member of FAIR Montréal, contributing to the company’s fundamental AI research efforts. Prior to that, he spent over a decade as a professor at McGill University in Electrical and Computer Engineering. He earned his PhD from the University of Wisconsin, Madison. Throughout his career he has worked on a variety of topics across signal processing, learning, and optimization, including distributed optimization and federated learning. His current research is focused on methods for training world models and using them for planning and control.

    Recording(Mila login required)

Organizers

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