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NeurIPS 2026UC San DiegoU California, San DiegoUC BerkeleyGoogle Deepmind / HarvardUC San Diego, AmazonVideo generation

Equilibrium Forcing: Adaptive Video Generation Without Noise Conditioning

Equilibrium Forcing removes noise conditioning from video diffusion to enable adaptive closed-loop inference that improves generation quality and consistency.

Hansen Lillemark, Alex Rojas, Zachary Novack, Runqian (Ray) Wang and 4 more

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

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8/20 AI panelreviewers recommend it

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AI panel: 8 of 20 reviewers recommend it
lenient 2/5
medium 5/10
strict 1/5
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NeurIPS 2026UC San DiegoU California, San DiegoAbiomedUC San Diego, AmazonOffline RL

Generative OOD-regularized Model-based Policy Optimization

GORMPO integrates generative density estimation into model-based offline RL to restrict policy updates to high-density dataset regions, improving performance by 17% on medical data while linking OOD detection quality to policy gains under stable dynamics.

Aysin Tumay, Jiahe Huang, Elise Jortberg, Rose Yu

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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12/20 AI panelreviewers recommend it

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AI panel: 12 of 20 reviewers recommend it
lenient 5/5
medium 7/10
strict 0/5
88%Must read
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NeurIPS 2026U California San DiegoU California, San DiegoMohamed bin Zayed University of UC San DiegoUC San Diego, AmazonAlignment & preference optimization

Calibrating LLMs with Semantic-level Reward

Calibration with Semantic Reward improves LLM calibration by replacing token-level confidence with direct semantic-space rewards, reducing ECE by up to 40% and raising AUROC by up to 31%.

Fengfei Yu, Ruijia Niu, Dongxia Wu, Yian Ma and 1 more

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · 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 5/5
medium 8/10
strict 2/5