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

• URL: http://arxiv.org/abs/2608.22045v1
• Date: Sat, 22 Aug 2026 17:16:29 GMT
• Title: Multi-Agent Discovery and Resource-Aware Autonomous Exploration of Scientific Datasets
• Abstract summary: WebVisus is a constrained and resource-aware multi-agent system for discovering and autonomously exploring remote, multiresolution scientific datasets.<n>Given a natural-language research question, WebVisus identifies the user's intent and launches an autonomous exploration agent.<n>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.
• Score: 5.767700537183031
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• More with Less: a Large Scale Remote Sensing VLM with a Simple Recipe [52.210953881548996]
  We show that a vision-language model can achieve competitive or state-of-the-art performance at challenging remote sensing benchmarks.<n>Our model uses a single language policy that can either answer directly in text or invoke a localization tool for segmentation and grounding.<n>Our approach achieves competitive results across a broad set of benchmarks, including high-resolution, multi-temporal, multi-modal and multi-view tasks.
  arXiv  Detail & Related papers  (2026-07-17T13:25:44Z)
• Towards Intelligent Geospatial Data Discovery: a knowledge graph-driven multi-agent framework powered by large language models [0.49157446832511503]
  This study proposes a knowledge graph-driven multi-agent framework for intelligent geospatial data discovery.<n>Results show that the framework substantially improves intent matching accuracy, ranking quality, recall, and discovery transparency compared with traditional systems.
  arXiv  Detail & Related papers  (2026-03-21T06:16:56Z)
• Understanding Usage and Engagement in AI-Powered Scientific Research Tools: The Asta Interaction Dataset [47.98539809308384]
  We analyze the Asta Interaction dataset, a large-scale resource comprising over 200,000 user queries and interaction logs.<n>We characterize query patterns, engagement behaviors, and how usage evolves with experience.<n>We release the anonymized dataset and analysis with a new query taxonomy to inform future designs of real-world AI research assistants.
  arXiv  Detail & Related papers  (2026-02-26T18:40:28Z)
• Enterprise Deep Research: Steerable Multi-Agent Deep Research for Enterprise Analytics [75.4712507893024]
  Enterprise Deep Research (EDR) is a multi-agent system that integrates a Master Planning Agent for adaptive query decomposition.<n>Four specialized search agents (General, Academic, GitHub, LinkedIn) and a visualization agent for data-driven insights are also included.<n>EDR reflects research direction with optional human-in-the-loop steering guidance.
  arXiv  Detail & Related papers  (2025-10-20T17:55:11Z)
• From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents [96.65646344634524]
  Large Language Models (LLMs), endowed with reasoning and agentic capabilities, are ushering in a new paradigm termed Agentic Deep Research.<n>We trace the evolution from static web search to interactive, agent-based systems that plan, explore, and learn.<n>We demonstrate that Agentic Deep Research not only significantly outperforms existing approaches, but is also poised to become the dominant paradigm for future information seeking.
  arXiv  Detail & Related papers  (2025-06-23T17:27:19Z)
• Deep Research Agents: A Systematic Examination And Roadmap [109.53237992384872]
  Deep Research (DR) agents are designed to tackle complex, multi-turn informational research tasks.<n>In this paper, we conduct a detailed analysis of the foundational technologies and architectural components that constitute DR agents.
  arXiv  Detail & Related papers  (2025-06-22T16:52:48Z)
• A Survey on Remote Sensing Foundation Models: From Vision to Multimodality [35.532200523631765]
  Vision and multimodal foundation models for remote sensing have significantly improved the capabilities of intelligent geospatial data interpretation.<n>The diversity in data types, the need for large-scale annotated datasets, and the complexity of multimodal fusion techniques pose significant obstacles to the effective deployment of these models.<n>This paper provides a review of the state-of-the-art in vision and multimodal foundation models for remote sensing, focusing on their architecture, training methods, datasets and application scenarios.
  arXiv  Detail & Related papers  (2025-03-28T01:57:35Z)
• Making Sense of Data in the Wild: Data Analysis Automation at Scale [0.1747623282473278]
  We propose a novel approach that combines intelligent agents with retrieval augmented generation to automate data analysis, dataset curation and indexing at scale.<n>We demonstrate that our approach results in more detailed dataset descriptions, higher hit rates and greater diversity in dataset retrieval tasks.
  arXiv  Detail & Related papers  (2025-01-27T10:04:10Z)
• Survey of Large Multimodal Model Datasets, Application Categories and Taxonomy [2.294223504228228]
  Multimodal learning, a rapidly evolving field in artificial intelligence, seeks to construct more versatile and robust systems.<n>Inspired by the human ability to assimilate information through many senses, this method enables applications such as text-to-video conversion, visual question answering, and image captioning.<n>Recent developments in datasets that support multimodal language models (MLLMs) are highlighted in this overview.
  arXiv  Detail & Related papers  (2024-12-23T18:15:19Z)
• DiscoveryBench: Towards Data-Driven Discovery with Large Language Models [50.36636396660163]
  We present DiscoveryBench, the first comprehensive benchmark that formalizes the multi-step process of data-driven discovery. Our benchmark contains 264 tasks collected across 6 diverse domains, such as sociology and engineering. Our benchmark, thus, illustrates the challenges in autonomous data-driven discovery and serves as a valuable resource for the community to make progress.
  arXiv  Detail & Related papers  (2024-07-01T18:58:22Z)
• Automated Requirements Relation Extraction [4.110571395660999]
  This chapter aims at providing a clear perspective on the theoretical and practical fundamentals in the field of natural language-based relation extraction.<n>We first describe the fundamentals of requirements relations based on the most relevant literature in the field, including the most common requirements relations types.<n>The core of the chapter is composed by two main sections: (i) natural language techniques for the identification and categorization of equirements relations (i.e., syntactic vs. semantic techniques) and (ii) information extraction methods for the task of relation extraction.
  arXiv  Detail & Related papers  (2024-01-22T16:14:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.