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Basile Terver
Doctorant chercheur
ParisAMI Labs
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Biography

Basile Terver is a doctoral researcher working at the intersection of artificial intelligence, robotics, and applied mathematics. Currently a PhD candidate with the Willow team at Inria and AMI – Advanced Machine Intelligence in Paris, his research focuses on next-generation machine learning architectures and robotic planning. His academic trajectory combines exceptional quantitative training with a strong interdisciplinary foundation, spanning École Polytechnique, the prestigious MVA master’s program at ENS Paris-Saclay, Sciences Po, and advanced studies in mathematics and physics at Sorbonne Université and the University of Toronto.

Basile has developed particularly close intellectual and research ties with Yann Le Cun, under whose supervision he conducted research at Meta before continuing his doctoral work within the broader ecosystem of AI research in Paris. His work on Joint-Embedding Predictive Architectures (JEPAs) reflects direct engagement with some of the most influential ideas shaping the future of AI.

He is also deeply connected to Pascale through the French academic and research ecosystem, combining scientific rigor with a rare ability to bridge technical, institutional, and strategic worlds. This combination positions him as part of a new generation of French AI researchers with both global ambition and strong roots in Europe’s leading intellectual networks.

Career History
2024-present
Inria
PhD candidate
Key Papers
This paper describes our research on AI agents embodied in visual, virtual or physical forms, enabling them to interact with both users and their environments. These agents, which include virtual avatars, wearable devices, and robots, are designed to perceive, learn and act within their surroundings, which makes them more similar to how humans learn and interact with the environments as compared to disembodied agents. We propose that the development of world models is central to reasoning and planning of embodied AI agents, allowing these agents to understand and predict their environment, to understand user intentions and social contexts, thereby enhancing their ability to perform complex tasks autonomously. World modeling encompasses the integration of multimodal perception, planning through reasoning for action and control, and memory to create a comprehensive understanding of the physical world. Beyond the physical world, we also propose to learn the mental world model of users to enable better human-agent collaboration.
2025 · arXivLabs
85 citations