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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.20809 |
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| _version_ | 1866909866002481152 |
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| author | Zou, Xueyan Ye, Jianglong Zhang, Hao Xiang, Xiaoyu Ding, Mingyu Yang, Zhaojing Lee, Yong Jae Tu, Zhuowen Liu, Sifei Wang, Xiaolong |
| author_facet | Zou, Xueyan Ye, Jianglong Zhang, Hao Xiang, Xiaoyu Ding, Mingyu Yang, Zhaojing Lee, Yong Jae Tu, Zhuowen Liu, Sifei Wang, Xiaolong |
| contents | With the rapid growth of research in AI and robotics now producing over 10,000 papers annually it has become increasingly difficult for researchers to stay up to date. Fast evolving trends, the rise of interdisciplinary work, and the need to explore domains beyond one's expertise all contribute to this challenge. To address these issues, we propose a generalizable pipeline capable of systematically analyzing any research area: identifying emerging trends, uncovering cross domain opportunities, and offering concrete starting points for new inquiry. In this work, we present Real Deep Research (RDR) a comprehensive framework applied to the domains of AI and robotics, with a particular focus on foundation models and robotics advancements. We also briefly extend our analysis to other areas of science. The main paper details the construction of the RDR pipeline, while the appendix provides extensive results across each analyzed topic. We hope this work sheds light for researchers working in the field of AI and beyond. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_20809 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Real Deep Research for AI, Robotics and Beyond Zou, Xueyan Ye, Jianglong Zhang, Hao Xiang, Xiaoyu Ding, Mingyu Yang, Zhaojing Lee, Yong Jae Tu, Zhuowen Liu, Sifei Wang, Xiaolong Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition Machine Learning With the rapid growth of research in AI and robotics now producing over 10,000 papers annually it has become increasingly difficult for researchers to stay up to date. Fast evolving trends, the rise of interdisciplinary work, and the need to explore domains beyond one's expertise all contribute to this challenge. To address these issues, we propose a generalizable pipeline capable of systematically analyzing any research area: identifying emerging trends, uncovering cross domain opportunities, and offering concrete starting points for new inquiry. In this work, we present Real Deep Research (RDR) a comprehensive framework applied to the domains of AI and robotics, with a particular focus on foundation models and robotics advancements. We also briefly extend our analysis to other areas of science. The main paper details the construction of the RDR pipeline, while the appendix provides extensive results across each analyzed topic. We hope this work sheds light for researchers working in the field of AI and beyond. |
| title | Real Deep Research for AI, Robotics and Beyond |
| topic | Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2510.20809 |