用多智能体自动探索科学数据，按资源动态调整精度。
Multi-Agent Discovery and Resource-Aware Autonomous Exploration of Scientific Datasets
- 自然语言提问后，智能体自主分层探测数据
- 根据内存和算力动态调整数据分辨率与质量
- 适合无专业背景的研究者快速发现数据
现代科学设施和仪器生成的数据规模巨大，个人研究者难以发现、访问和探索。尽管许多数据集公开可用，但使用它们通常需要熟悉存储结构、数据格式、多分辨率层级及可视化参数。我们提出WebVisus，一个受约束且资源感知的多智能体系统，用于发现并自主探索远程、多分辨率科学数据集。给定自然语言研究问题，WebVisus识别用户意图并启动自主探索代理，在不下载完整数据集的情况下，动态调整数据分辨率与检索质量，逐层检查切片、体积和时间步。该设计支持渐进式探索，无需手动配置低层可视化参数。我们报告了系统架构、受限智能体协议、资源感知访问机制，并通过案例研究评估了在多个科学数据集上的自主视觉探索与资源感知代理访问能力。
原文摘要 · Abstract (English)
Modern scientific facilities and instruments generate datasets at scales that are difficult for individual researchers to discover, access, and explore. Although many datasets are publicly available, using them often requires familiarity with repository organization, data formats, multiresolution structures, and visualization parameters. We present WebVisus, a constrained and resource-aware multi-agent system for discovering and autonomously exploring remote, multiresolution scientific datasets. Given a natural-language research question, WebVisus identifies the user's intent and launches an autonomous exploration agent that examines slices, volumes, and timesteps while adapting data resolution and retrieval quality to available client memory and computational resources. This design supports progressive exploration without complete dataset downloads or manual configuration of low-level visualization parameters using natural languages. We report the system architecture, constrained agent protocol, resource-aware access mechanism, and case studies evaluating autonomous visual exploration and resource-aware agentic access across scientific datasets.
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