Yunshu Wu — accepted AI-conference papers

2026

Multiverse Mechanica: A Testbed for Learning Game Mechanics via Counterfactual Worlds

Robert Ness, Ricardo Cannizzaro, Yunshu Wu, Lars Kunze

ICLR 2026poster

We study how generative world models trained on video games can go beyond mere reproduction of gameplay visuals to learning game mechanics—the modular rules that causally govern gameplay. We introduce a formalization of the concept of game mechanics that operationalizes mechanic-learning as a causal…

Cited by 0 Source ↗Scholar ↗

2024

Your Diffusion Model is Secretly a Noise Classifier and Benefits from Contrastive Training

Yunshu Wu, Yingtao Luo, Xianghao Kong, Evangelos E. Papalexakis, Greg Ver Steeg

NeurIPS 2024poster

Diffusion models learn to denoise data and the trained denoiser is then used to generate new samples from the data distribution. In this paper, we revisit the diffusion sampling process and identify a fundamental cause of sample quality degradation: the denoiser is poorly estimated in regions that…

Cited by 1 Source ↗PDF ↗code ↗Scholar ↗