Good Papers

Showing papers from Mila / Université de Montréal Show all papers

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Do Enterprise Systems Need Learned World Models? The Importance of Context to Infer Dynamics

Jishnu S Nair, Patrice Bechard, Rishabh Maheshwary, SRAVAN RAMACHANDRAN and 13 more

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
83%Must read
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Representation Learning Enables Scalable Multitask Deep Reinforcement Learning

Predictive representation learning combined with high-capacity value approximation drives scalable multitask RL, with the simple model-free MR.Q outperforming world-model methods across continuous control tasks.

Johan Obando Ceron, Lu Li, Scott Fujimoto, Pierre-Luc Bacon and 2 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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13/20 AI panelreviewers recommend it

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AI panel: 13 of 20 reviewers recommend it
lenient 4/5
medium 8/10
strict 1/5
88%Must read
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Bidirectional Information Flow (BIF) - A Sample Efficient Hierarchical Gaussian Process for Bayesian Optimization

Bidirectional Information Flow enables continuous two-way communication in hierarchical Gaussian processes for Bayesian optimization, improving sample efficiency, training robustness, and modular subtask reuse while significantly outperforming unidirectional and vanilla methods.

Juan D. Guerra, Thomas Garbay, Numa Dancause, Guillaume Lajoie and 1 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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15/20 AI panelreviewers recommend it

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AI panel: 15 of 20 reviewers recommend it
lenient 4/5
medium 9/10
strict 2/5
71%Highly rated
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A Mechanistic Analysis of Looped Reasoning Language Models

Looped reasoning models converge to cyclic fixed points that stabilize attention and repeat feedforward inference stages iteratively, with recurrence size and normalization affecting stability.

Hugh Blayney, Alvaro Arroyo, Johan Obando Ceron, Pablo Samuel Castro and 3 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · ▲ 1 on Hugging Face

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6/20 AI panelreviewers recommend it

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AI panel: 6 of 20 reviewers recommend it
lenient 3/5
medium 3/10
strict 0/5
86%Must read
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Local Guidance, Global Impact: Gaussian-Reshaped Trust Region Unlocks Behavior Transitions

Gaussian trust region reshaping replaces monotonic divergence penalties with bounded non-monotonic constraints, unlocking efficient behavior transitions in non-stationary reinforcement learning.

Bingxu Liu, Jiashun Liu, Johan Obando Ceron, Hao Wang and 4 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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14/20 AI panelreviewers recommend it

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AI panel: 14 of 20 reviewers recommend it
lenient 4/5
medium 9/10
strict 1/5