Related papers: Real Deep Research for AI, Robotics and Beyond

Real Deep Research for AI, Robotics and Beyond

URL: http://arxiv.org/abs/2510.20809v1
Date: Thu, 23 Oct 2025 17:59:05 GMT
Title: Real Deep Research for AI, Robotics and Beyond
Authors: Xueyan Zou, Jianglong Ye, Hao Zhang, Xiaoyu Xiang, Mingyu Ding, Zhaojing Yang, Yong Jae Lee, Zhuowen Tu, Sifei Liu, Xiaolong Wang,
Abstract summary: We present Real Deep Research (RDR) a comprehensive framework applied to the domains of AI and robotics.<n>The main paper details the construction of the RDR pipeline, while the appendix provides extensive results across each analyzed topic.
Score: 85.87181330763548
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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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