17 Omana Kuttan, Manjunath Zhou, Kai Steinheimer, Jan Stöcker, Horst TES Physical review / C Physical review / C Inst. Woodbury, NY 2469-9985 10.1103/wyq5-hlp5 054907 5 112 We present a novel deep generative framework that uses probabilistic diffusion models for ultrafast, event-by-event simulations of heavy-ion collision output. This new framework is trained on ultrarelativistic quantum molecular dynamics (UrQMD) cascade data to generate a full collision event output containing 26 distinct hadron species. The output is represented as a point cloud, where each point is defined by a particle's momentum vector and its corresponding species information. Our architecture integrates a normalizing flow-based condition generator that encodes global event features into a latent vector, and a diffusion model that synthesizes a point cloud of particles based on this condition. A detailed description of the model and an in-depth analysis of its performance is provided. The conditional point-cloud diffusion model learns to generate realistic output particles of collision events which successfully reproduce the UrQMD distributions for multiplicity, momentum, and rapidity of each hadron type. The flexible point-cloud representation of the event output preserves full event-level granularity, enabling direct application to inverse problems and parameter estimation tasks while also making it easily adaptable for accelerating any event-by-event model calculation or detector simulation. Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Funded by SCOAP3. ; PUB:(DE-HGF)16, ; 0, ; WOS:001630418000005 Journal Article 2025 GSI-2026-00011 2025 https://repository.gsi.de/record/363801 https://doi.org/10.1103/wyq5-hlp5 <Go to ISI>://WOS:001630418000005