Real Deep Research for AI, Robotics and Beyond
October 23, 2025 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Xueyan Zou, Jianglong Ye, Hao Zhang, Xiaoyu Xiang, Mingyu Ding, Zhaojing Yang, Yong Jae Lee, Zhuowen Tu, Sifei Liu, Xiaolong Wang
arXiv ID
2510.20809
Category
cs.AI: Artificial Intelligence
Cross-listed
cs.CL,
cs.CV,
cs.LG
Citations
0
Venue
arXiv.org
Last Checked
4 months ago
Abstract
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.
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