Position: Shifting AI Efficiency From Model-Centric to Data-Centric Compression

The advancement of large language models (LLMs) and multi-modal LLMs (MLLMs) has historically relied on scaling model parameters. However, as hardware limits constrain further model growth, the primary computational bottleneck has shifted to the quadratic cost of self-attention over increasingly long sequences. In this position paper, we argue that the focus of research for efficient artificial intelligence (AI) is shifting from model-centric compression to data-centric compression. We position data-centric compression as the emerging paradigm, which improves AI efficiency by directly compressing the volume of data processed during model training or inference.

@article{liu2026shifting,
  title={Shifting AI Efficiency From Model-Centric to Data-Centric Compression},
  author={Liu, Xuyang and Wen, Zichen and Wang, Shaobo and Chen, Junjie and Tao, Zhishan and Wang, Yubo and Jin, Xiangqi and Zou, Chang and Wang, Yiyu and Liao, Chenfei and Zheng, Xu and Chen, Honggang and Li, Weijia and Hu, Xuming and He, Conghui and Zhang, Linfeng},
  journal={arXiv preprint arXiv:2505.19147},
  year={2026}
}