# Large-scale Generative Simulation Artificial Intelligence: the Next Hotspot in Generative AI

arXiv: https://arxiv.org/abs/2308.02561 · HTML: https://mlsanity.com/paper/2308.02561/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 6 references that are in the map.
- First posted on arXiv 3 Aug 2023.

## Builds on (most cited first, 1 of 1)

- [Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges](https://mlsanity.com/paper/2104.13478/) (2021)

## Built on by (most cited first, 5 of 5)

- [Model Predictive Task Sampling for Efficient and Robust Adaptation](https://mlsanity.com/paper/2501.11039/) (2025)
- [Robust Fast Adaptation from Adversarially Explicit Task Distribution Generation](https://mlsanity.com/paper/2407.19523/) (2024)
- [A Simple Yet Effective Strategy to Robustify the Meta Learning Paradigm](https://mlsanity.com/paper/2310.00708/) (2023)
- [Theoretical Investigations and Practical Enhancements on Tail Task Risk Minimization in Meta Learning](https://mlsanity.com/paper/2410.22788/) (2024)
- [Group & Reweight: A Novel Cost-Sensitive Approach to Mitigating Class Imbalance in Network Traffic Classification](https://mlsanity.com/paper/2409.19214/) (2024)

## Abstract (arXiv)

The concept of GenAI has been developed for decades. Until recently, it has impressed us with substantial breakthroughs in natural language processing and computer vision, actively engaging in industrial scenarios. Noticing the practical challenges, e.g., limited learning resources, and overly dependencies on scientific discovery empiricism, we nominate large-scale generative simulation artificial intelligence (LS-GenAI) as the next hotspot for GenAI to connect.