用系统化方法追踪AI与机器人学前沿，助研究者快速定位新方向。
Real Deep Research for AI, Robotics and Beyond
- 构建可复用的分析管道，自动识别研究趋势与跨领域机会。
- 覆盖超1万篇/年论文，聚焦基础模型与机器人进展，提供具体研究切入点。
- 适合追赶前沿或跨领域探索的研究人员，尤其对大模型与机器人方向有帮助。
随着人工智能与机器人学研究的迅猛发展，每年产出超过10,000篇论文，研究人员难以及时跟进。快速演变的趋势、跨学科工作的兴起，以及探索自身专业之外领域的需要，加剧了这一挑战。为此，我们提出一个通用可扩展的分析流程——真实深度研究（Real Deep Research, RDR），能够系统性地分析任意研究领域，识别新兴趋势、发现跨领域机遇，并为新研究提供具体起点。本文将RDR框架应用于人工智能与机器人学领域，重点关注基础模型与机器人进展，同时简要拓展至其他科学领域。主文详述RDR流程构建，附录则展示各主题的详尽分析结果。我们希望此项工作能为人工智能及相关领域的研究者提供启示。
原文摘要 · Abstract (English)
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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