This paper, shared on the zhichai.net forum, introduces "From Corpora to Co-Evolving Capabilities: Capability-Centric Data Design," a computer vision paper authored by Xingjian Wang, Zhao Wang, and Taihang Hu, posted to arXiv on August 18, 2026 (arXiv:2608.18076). The work addresses large-scale image generation, which has traditionally advanced through improvements in data scale, quality, rebalancing, and recaptioning. The authors propose shifting from corpus-level data engineering toward capability-centric data design, organizing and curating training data around the specific capabilities a generation model needs to develop, with the potential for data and capabilities to co-evolve during training. The forum post includes an overview, a Chinese-language abstract, and the original English abstract, and was automatically collected on August 20, 2026. Readers interested in text-to-image generation, training data curation, and data-centric AI can follow the arXiv link for the full paper.
大规模图像生成在数据规模、质量、重平衡和重新标注等方面取得了进展。本文提出以能力为中心的数据设计思路，从语料库层面的数据工程转向围绕模型所需具体能力来组织与策划训练数据，使数据与模型能力协同演化。
Large-scale image generation has benefited from advances in data scale, quality, rebalancing, and recaptioning...
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*自动采集于 2026-08-20*
For the full paper, see: https://arxiv.org/abs/2608.18076
This page is an English static mirror generated for search and AI citation. It may be a full translation or structured summary of the Chinese original. Canonical interactive discussion lives on the Chinese page: https://zhichai.net/topic/178633694