Hypothesis Exploration with LLM-driven Intelligent Orchestration System
Summary: HELIOS combines multi-agent LLM orchestration with statistically grounded hypothesis discovery for interactive visual exploration. It ranks candidates by coverage, diversity, impact, and homogeneity, then synthesizes narratives and follow-up analyses to avoid hallucinations. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Bruno G. Tavares dos Santos (Federal University of Rio Grande do Sul)
- 2. Vicente Nejar de Almeida (Federal University of Rio Grande do Sul)
- 3. Eduardo Ribeiro (University of Tocantins)
- 4. Sihem Amer-Yahia (CNRS; University Grenoble Alpes)
- 5. João Luiz Dihl Comba (Federal University of Rio Grande do Sul)
BibTeX Citation
@article{santos_vldb26,
title = {{Hypothesis Exploration with LLM-driven Intelligent Orchestration System}},
author = {Santos, Bruno G. Tavares dos and de Almeida, Vicente Nejar and Ribeiro, Eduardo and Amer-Yahia, Sihem and Comba, João Luiz Dihl},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {12},
pages = {4814--4817},
doi = {10.14778/3827998.3828129},
url = {https://doi.org/10.14778/3827998.3828129},
year = {2026}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,807 | SHEVA: A Visual Analytics System for Statistical Hypothesis Exploration | 2023 | VLDB | 4.9793485e-05 |
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