Multi-Dimensional Knowledge Profiling with Large-Scale Literature Database and Hierarchical Retrieval
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866913031694319616 |
|---|---|
| author | Xue, Zhucun Zhang, Jiangning Jiang, Juntao Liu, Jinzhuo He, Haoyang Hu, Teng Hu, Xiaobin Liu, Yong Yan, Shuicheng |
| author_facet | Xue, Zhucun Zhang, Jiangning Jiang, Juntao Liu, Jinzhuo He, Haoyang Hu, Teng Hu, Xiaobin Liu, Yong Yan, Shuicheng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_15170 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Multi-Dimensional Knowledge Profiling with Large-Scale Literature Database and Hierarchical Retrieval Xue, Zhucun Zhang, Jiangning Jiang, Juntao Liu, Jinzhuo He, Haoyang Hu, Teng Hu, Xiaobin Liu, Yong Yan, Shuicheng Computer Vision and Pattern Recognition 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. |
| title | Multi-Dimensional Knowledge Profiling with Large-Scale Literature Database and Hierarchical Retrieval |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.15170 |