# Multi-Agent Discovery and Resource-Aware Autonomous Exploration of Scientific Datasets

arXiv: https://arxiv.org/abs/2608.22045 · HTML: https://mlsanity.com/paper/2608.22045/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 41 references that are in the map.
- First posted on arXiv 22 Aug 2026.

## Builds on (most cited first, 27 of 27)

- [The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery](https://mlsanity.com/paper/2408.06292/) (2024)
- [Agent Laboratory: Using LLM Agents as Research Assistants](https://mlsanity.com/paper/2501.04227/) (2025)
- [Agentic AI for Scientific Discovery: A Survey of Progress, Challenges, and Future Directions](https://mlsanity.com/paper/2503.08979/) (2025)
- [NL4DV: A Toolkit for Generating Analytic Specifications for Data Visualization from Natural Language Queries](https://mlsanity.com/paper/2008.10723/) (2020)
- [ChartGPT: Leveraging LLMs to Generate Charts from Abstract Natural Language](https://mlsanity.com/paper/2311.01920/) (2023)
- [Towards Natural Language Interfaces for Data Visualization: A Survey](https://mlsanity.com/paper/2109.03506/) (2021)
- [LEVA: Using Large Language Models to Enhance Visual Analytics](https://mlsanity.com/paper/2403.05816/) (2024)
- [NLI4VolVis: Natural Language Interaction for Volume Visualization via LLM Multi-Agents and Editable 3D Gaussian Splatting](https://mlsanity.com/paper/2507.12621/) (2025)
- [XNLI: Explaining and Diagnosing NLI-based Visual Data Analysis](https://mlsanity.com/paper/2301.10385/) (2023)
- [Empowering Scientific Workflows with Federated Agents](https://mlsanity.com/paper/2505.05428/) (2025)
- [Fine-Tuned Large Language Model for Visualization System: A Study on Self-Regulated Learning in Education](https://mlsanity.com/paper/2407.20570/) (2024)
- [ParaView-MCP: An Autonomous Visualization Agent with Direct Tool Use](https://mlsanity.com/paper/2505.07064/) (2025)
- [Autonomous Agents for Scientific Discovery: Orchestrating Scientists, Language, Code, and Physics](https://mlsanity.com/paper/2510.09901/) (2025)
- [Quda: Natural Language Queries for Visual Data Analytics](https://mlsanity.com/paper/2005.03257/) (2020)
- [PhenoFlow: A Human-LLM Driven Visual Analytics System for Exploring Large and Complex Stroke Datasets](https://mlsanity.com/paper/2407.16329/) (2024)
- [Empowering Local Communities Using Artificial Intelligence](https://mlsanity.com/paper/2110.02007/) (2021)
- [Visualizationary: Automating Design Feedback for Visualization Designers using LLMs](https://mlsanity.com/paper/2409.13109/) (2024)
- [InferA: A Smart Assistant for Cosmological Ensemble Data](https://mlsanity.com/paper/2510.12920/) (2025)
- [Scalable Volume Visualization for Big Scientific Data Modeled by Functional Approximation](https://mlsanity.com/paper/2312.15073/) (2023)
- [Web-based Visualization and Analytics of Petascale data: Equity as a Tide that Lifts All Boats](https://mlsanity.com/paper/2408.11831/) (2024)
- [Employing Artificial Intelligence to Steer Exascale Workflows with Colmena](https://mlsanity.com/paper/2408.14434/) (2024)
- [Connecting Large Language Model Agent to High Performance Computing Resource](https://mlsanity.com/paper/2502.12280/) (2025)
- [MisVisFix: An Interactive Dashboard for Detecting, Explaining, and Correcting Misleading Visualizations using Large Language Models](https://mlsanity.com/paper/2508.04679/) (2025)
- [Comprehensive and Comprehensible Data Catalogs: The What, Who, Where, When, Why, and How of Metadata Management](https://mlsanity.com/paper/2103.07532/) (2021)
- [Scalable Climate Data Analysis: Balancing Petascale Fidelity and Computational Cost](https://mlsanity.com/paper/2507.08006/) (2025)
- [Large Data Acquisition and Analytics at Synchrotron Radiation Facilities](https://mlsanity.com/paper/2602.05837/) (2026)
- [Animating Petascale Time-varying Data on Commodity Hardware with LLM-assisted Scripting](https://mlsanity.com/paper/2603.07053/) (2026)

## Abstract (arXiv)

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.