NORA是专为地理空间数据科学设计的自动研究代理，提升科研效率与质量。
NORA: A Harness-Engineered Autonomous Research Agent for End-to-End Spatial Data Science
- 采用21个领域专用技能和9个专家子代理，实现全流程自动化
- 在7个维度评估中显著优于通用代理配置，提升研究质量和效率
- 适合地理信息科学、空间数据分析等领域的研究人员使用
自动化科学工作流已成为人工智能的重要前沿，但现有自主研究代理多为通用型，缺乏空间数据科学所需的专门推理、方法选择与数据获取能力。本文提出NORA（Night Owl Research Agent），一种专为地理信息科学与空间数据科学设计的多代理自主研究系统。NORA通过包含21个领域专用工作流技能、9个专家子代理及定制的模型上下文协议（MCP）服务器的技能优先架构，完成完整研究生命周期。系统核心创新包括：一个编码探索性空间数据分析、空间回归与诊断决策框架的空间分析技能单元；一个支持从权威地理空间数据源可复现下载的空间数据获取技能。本文提出“牵引工程”概念，通过生命周期钩子、安全闸门、生成-评估分离、人机协同与状态持久化，确保自主研究的可靠性与可复现性。我们通过6位领域专家与3名LLM评审，在7个维度（新颖性、质量、严谨性等）进行案例评估，结果表明领域专用牵引工程显著提升研究输出的效率与质量，优于通用代理配置。
原文摘要 · Abstract (English)
The automation of scientific research workflows has emerged as a transformative frontier in artificial intelligence, yet existing autonomous research agents remain largely domain-agnostic, lacking the specialized reasoning, method selection, and data acquisition capabilities required for rigorous spatial data science. This paper introduces NORA (Night Owl Research Agent), a harness-engineered, multi-agent autonomous research system purpose-built for GIScience and spatial data science. NORA orchestrates the complete research lifecycle through a skills-first architecture comprising 21 domain-specialized workflow skills, 9 specialist sub-agents, and custom Model Context Protocol (MCP) servers. Central to the system's design are two novel domain-specialized skills: a spatial analysis skill unit that encodes decision frameworks for exploratory spatial data analysis, spatial regression, and diagnostics; and a spatial data download skill that supports reproducible acquisition from authoritative geospatial data sources. We formalize the concept of harness engineering for scientific research agents, demonstrating how lifecycle hooks, safety gates, generator-evaluator separation, human-in-the-loop, and state persistence ensure reliable and reproducible autonomous research. We evaluate NORA through case studies by 6 domain specialists and 3 LLM reviewers across seven dimensions (novelty, quality, rigor, etc). Results demonstrate that domain-specialized harness engineering substantially improves the efficiency and quality of research output compared to general-purpose agent configurations.
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