用多智能体系统实现宇宙学数据的高效交互式分析
InferA: A Smart Assistant for Cosmological Ensemble Data
- 构建监督智能体协调多个专业代理分阶段处理数据
- 在数TB级宇宙学模拟数据上实现高效交互分析
- 适合需要处理大规模科学数据的研究者使用
大规模科学数据集因体量庞大、结构复杂且需领域专业知识，分析难度高。现有自动化工具如PandasAI通常需全量加载数据，缺乏对整体数据结构的理解，难以作为千兆字节级数据的智能分析助手。为此，我们提出InferA，一种基于大语言模型的多智能体系统，实现可扩展、高效的科学数据分析。其核心为监督智能体，协调一组专业化代理完成数据检索与分析的各个阶段。系统通过与用户交互明确分析意图并确认查询目标，确保用户目标与系统行为一致。我们以包含数TB数据的HACC宇宙学模拟集合验证了该框架的可用性。
原文摘要 · Abstract (English)
Analyzing large-scale scientific datasets presents substantial challenges due to their sheer volume, structural complexity, and the need for specialized domain knowledge. Automation tools, such as PandasAI, typically require full data ingestion and lack context of the full data structure, making them impractical as intelligent data analysis assistants for datasets at the terabyte scale. To overcome these limitations, we propose InferA, a multi-agent system that leverages large language models to enable scalable and efficient scientific data analysis. At the core of the architecture is a supervisor agent that orchestrates a team of specialized agents responsible for distinct phases of the data retrieval and analysis. The system engages interactively with users to elicit their analytical intent and confirm query objectives, ensuring alignment between user goals and system actions. To demonstrate the framework's usability, we evaluate the system using ensemble runs from the HACC cosmology simulation which comprises several terabytes.
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