Semantic occupancy has emerged as a powerful representation in world models for its ability to capture rich spatial semantics. However, most existing occupancy world models rely on static and fixed embeddings or grids, which inherently limit the flexibility of perception. Moreover, their “in-place classification” over grids exhibits a potential misalignment with the dynamic and continuous nature of real scenarios. In this paper, we propose SparseWorld, a novel 4D occupancy world model that is flexible, adaptive, and efficient, powered by sparse and dynamic queries. We propose a Range-Adaptive Perception module, in which learnable queries are modulated by the ego vehicle states and enriched with temporal-spatial associations to enable extended-range perception. To effectively capture the dynamics of the scene, we design a State-Conditioned Forecasting module, which replaces classification-based forecasting with regressionguided formulation, precisely aligning the dynamic queries with the continuity of the 4D environment. In addition, We specifically devise a Temporal-Aware Self-Scheduling training strategy to enable smooth and efficient training. Extensive experiments demonstrate that SparseWorld achieves state-ofthe-art performance across perception, forecasting, and planning tasks. Comprehensive visualizations and ablation studies further validate the advantages of SparseWorld in terms of flexibility, adaptability, and efficiency.

Overview

News

• 2026/1/13: We’ve released an upgraded version of SparseWorld, called SparseOccVLA (code, paper), which successfully integrates sparse occupancy queries into LLMs. Feel free to check it out!
• 2025/12/20: We release the inference and training code as well as the pretrained weight!
• 2025/11/8: SparseWorld is accepted by AAAI 2026 🎉🎉!
• 2025/10.10: The paper is released on arXiv.

Getting Started

• Installation

• Prepare Dataset

• Train and Eval

Model Zoo

Method	Config	Avg mIoU	Avg IoU	log	Checkpoints
SparseWorld-R50	config	13.20	22.03	log	model

Here, the model was trained on 8 H20 GPUs, while it only uses about 17 GB of GPU memory in practice, which means our results can be reproduced on consumer-grade GPUs such as the RTX 4090.

Results and Visualizations

• Results

• Comparative Visualizations

Acknowledgement

Our code is developed based of following open source codebases:

• OPUS
• PreWorld

We sincerely appreciate their outstanding works.

Citation

If you find our work helpful or interesting, don’t forget to give us a ⭐. Thanks for your support!

If this work is helpful for your research, please consider citing:

@article{dang2025sparseworld,
  title={SparseWorld: A Flexible, Adaptive, and Efficient 4D Occupancy World Model Powered by Sparse and Dynamic Queries},
  author={Dang, Chenxu and Liu, Haiyan and Bao, Guangjun and An, Pei and Tang, Xinyue and Ma, Jie and Sun, Bingchuan and Wang, Yan},
  journal={arXiv preprint arXiv:2510.17482},
  year={2025}
}

@article{dang2026sparseoccvla,
  title={SparseOccVLA: Bridging Occupancy and Vision-Language Models via Sparse Queries for Unified 4D Scene Understanding and Planning}, 
  author={Dang, Chenxu and Wang, Jie and Guang, Li and Zihan, You and Hangjun, Ye and Jie, Ma and Long, Chen and Yan, Wang},
  journal={arXiv preprint arXiv:2601.06474},
  year={2026}
}