Good Papers

Showing papers from Universite de Montreal Show all papers

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What Transformer FFNs Never See: Theory, Diagnosis, and Lightweight Remediation

Tinghe Zhang, Yucheng Xiao, Alex Lamb

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

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0/20 AI panelreviewers recommend it

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lenient 0/5
medium 0/10
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86%Must read
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SLOPE: Optimistic Potential Landscape Shaping for Model-based Reinforcement Learning

SLOPE constructs optimistic potential landscapes via distributional regression to amplify sparse success signals and guide planning, outperforming baselines across sparse reward benchmarks.

Yao-Hui Li, Zeyu Wang, Xin Li, Wei Pang and 6 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 5/5
medium 7/10
strict 2/5
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Next-Latent Prediction Transformers Learn Compact World Models

NextLat adds latent self-prediction to transformers, theoretically converging to belief states and empirically improving world modeling, reasoning, and inference speed.

Jayden Teoh, Manan Tomar, Kwangjun Ahn, Edward Hu and 6 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026 · ▲ 7 on Hugging Face · Code ★ 196

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16/20 AI panelreviewers recommend it

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AI panel: 16 of 20 reviewers recommend it
lenient 4/5
medium 10/10
strict 2/5