用10万篇论文构建多维研究图谱，看清AI领域发展脉络。
Multi-Dimensional Knowledge Profiling with Large-Scale Literature Database and Hierarchical Retrieval
- 整合22个顶会论文，用大模型与分层检索分析文本内容
- 发现安全、多模态推理等方向兴起，翻译与图方法趋于稳定
- 适合关注AI趋势、选题规划的研究者使用
机器学习、视觉和语言等领域研究的快速扩展，导致文献数量激增，传统引文分析工具仅依赖元数据，难以揭示论文语义内容，难追踪主题演变或跨领域影响。为更清晰地把握最新进展，我们整理了2020至2025年间22个主要会议超过10万篇论文，构建多维度知识画像流程，结合主题聚类、大模型辅助解析与结构化检索，生成研究活动的综合表征，支持对主题生命周期、方法演进、数据集与模型使用模式及机构研究方向的分析。分析显示，安全、多模态推理和代理导向研究显著增长，而神经机器翻译和基于图的方法逐渐趋于稳定。这些发现提供了AI研究演化的实证视角，并为理解宏观趋势与识别新兴方向提供资源。
原文摘要 · Abstract (English)
The rapid expansion of research across machine learning, vision, and language has produced a volume of publications that is increasingly difficult to synthesize. Traditional bibliometric tools rely mainly on metadata and offer limited visibility into the semantic content of papers, making it hard to track how research themes evolve over time or how different areas influence one another.To obtain a clearer picture of recent developments, we compile a unified corpus of more than 100,000 papers from 22 major conferences between 2020 and 2025 and construct a multidimensional profiling pipeline to organize and analyze their textual content. By combining topic clustering, LLM-assisted parsing, and structured retrieval, we derive a comprehensive representation of research activity that supports the study of topic lifecycles, methodological transitions, dataset and model usage patterns, and institutional research directions.Our analysis highlights several notable shifts, including the growth of safety, multimodal reasoning, and agent-oriented studies, as well as the gradual stabilization of areas such as neural machine translation and graph-based methods. These findings provide an evidence-based view of how AI research is evolving and offer a resource for understanding broader trends and identifying emerging directions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。