arXiv cs.AI· Yutong Leng, Haojiong Chen, Fukai Shang, Xiaoyang Wang, Mindong Liu, Runyuan Ma, Jiantao Qiu·· 2 天前AI 评分32
SPRA：面向 AI 智能体的结构化论文表示，覆盖超百万篇 AI 论文
Do Agents Need a New Kind of Paper? SPRA: Structured Paper Representation for Agents
AI 导读
研究者提出 SPRA（Structured Paper Representation for Agents），一种以持久化研究对象和类型化关系组织的论文级表示，其 empirical-AI 配置涵盖 Problems、Contributions、Components、ExperimentSetups、Measures 和 Findings。
正文 · 原文
Abstract:Scientific agents must connect research problems, methods, and findings dispersed through narrative articles. We introduce SPRA (Structured Paper Representation for Agents), a paper-level representation of persistent research objects and typed relations. Its empirical-AI profile organizes Problems, Contributions, Components, ExperimentSetups, Measures, and Findings for querying, traversal, and reuse. Selected paper-level citation relations record dependencies, component use, and comparisons with prior work; source locations support verification. We instantiate SPRA across more than one million AI papers, with 19.95 million research units and 24.07 million internal relations. Evaluations access SPRA through Schema-RAG with task evidence fixed before inference. Across seven subfields, SPRA improves source-conditioned scientific QA by 0.381/5 over passage retrieval while remaining close to Full Compact (0.078/5 lower on average). In directed cross-paper reasoning, qualification improves by 0.184/5 over passage retrieval; Top-5 synthesis shows mixed quality effects. Across all paired RQ2 tasks, including Top-5, input+output tokens decrease by 28.5% relative to passage retrieval and 95.3% relative to Full Compact. In idea generation, a 0.067-point lower Overall score than Full Compact accompanies 93.7% fewer input+output tokens. Together, these results support SPRA as a usable representation of scientific content and a foundation for research workflows that connect, inspect, and reuse paper-level research objects. The SPRA resource is accessible through the SciVerse scholarly-data platform.
| Comments: | 23 pages, including references and appendices; resource website: this https URL |
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.21424 [cs.AI] |
| (or arXiv:2610.21424v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.21424 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yutong Leng [view email]
[v1]
Thu, 15 Oct 2026 07:34:12 UTC (549 KB)
来源：arXiv cs.AI · arxiv.org