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NeurIPS 2026CornellTogether AICornell University / ASAPP ReseaDiffusion models

Empowering Masked Diffusion Models to Self-Correct with Leave-One-Out Transformers

Sofian Zalouk, Vincent Counathe, Paul Jünger, Daniel Cao and 3 more

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

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57%Worth a look
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NeurIPS 2026Together AIJohns HopkinsPrincetonUC San Diego, Together AIQuantization

NyoomFloat12: Accelerating LLM Inference via Lossless 12-bit Weight Compression

Sylvie Liberman, Xinyu Fang, Tianyi Zhang, Tri Dao and 1 more

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

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1/20 AI panelreviewers recommend it

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89%Must read
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NeurIPS 2026StanfordTogether AIGoogle BrainStanfordOn-device ML

Intelligence per Watt: Measuring Intelligence Efficiency of Local AI

Proposing intelligence per watt to evaluate local LLM inference, the study finds local models answer 88.7% of queries with 5.3x efficiency gains since 2023 but remain 1.4x less efficient than cloud accelerators.

Jon Saad-Falcon, Avanika Narayan, Hakki Akengin, J. W Griffin and 10 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026 · ▲ 17 on Hugging Face · Code ★ 95

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16/20 AI panelreviewers recommend it

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AI panel: 16 of 20 reviewers recommend it
lenient 5/5
medium 7/10
strict 4/5