Setting SAIL: Leveraging Scientist-AI-Loops for Rigorous Visualization Tools
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2026
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866915890564431872 |
|---|---|
| author | Schuster, Nico Salcedo, Andrés N. Bouchard, Simon Frei, Dennis Pisani, Alice Bautista, Julian E. Zoubian, Julien Escoffier, Stephanie Liu, Wei Valogiannis, Georgios Zarrouk, Pauline |
| author_facet | Schuster, Nico Salcedo, Andrés N. Bouchard, Simon Frei, Dennis Pisani, Alice Bautista, Julian E. Zoubian, Julien Escoffier, Stephanie Liu, Wei Valogiannis, Georgios Zarrouk, Pauline |
| contents | Scientists across all disciplines share a common challenge: the divide between their theoretical knowledge and the specialized skills and time needed to build interactive tools to communicate this expertise. While large language models (LLMs) offer unparalleled acceleration in code generation, they frequently prioritize functional syntax over scientific accuracy, risking visually convincing but scientifically invalid results. This work advocates the Scientist-AI-Loop (SAIL), a framework designed to harness this speed without compromising rigor. By separating domain logic from code syntax, SAIL enables researchers to maintain strict oversight of scientific concepts and constraints while delegating code implementation to AI. We illustrate this approach through two open-source, browser-based astrophysics tools: an interactive gravitational lensing visualization and a large-scale structure formation sandbox, both publicly available. Our methodology condensed development to mere days while maintaining scientific integrity. We specifically address failure modes where AI-generated code neglects phenomenological boundaries or scientific validity. While cautioning that research-grade code requires stringent protocols, we demonstrate through two examples that SAIL provides an effective code generation workflow for outreach, teaching, professional presentations, and early-stage research prototyping. This framework contributes to a foundation for the further development of AI-assisted scientific software. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_18145 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Setting SAIL: Leveraging Scientist-AI-Loops for Rigorous Visualization Tools Schuster, Nico Salcedo, Andrés N. Bouchard, Simon Frei, Dennis Pisani, Alice Bautista, Julian E. Zoubian, Julien Escoffier, Stephanie Liu, Wei Valogiannis, Georgios Zarrouk, Pauline Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Human-Computer Interaction Scientists across all disciplines share a common challenge: the divide between their theoretical knowledge and the specialized skills and time needed to build interactive tools to communicate this expertise. While large language models (LLMs) offer unparalleled acceleration in code generation, they frequently prioritize functional syntax over scientific accuracy, risking visually convincing but scientifically invalid results. This work advocates the Scientist-AI-Loop (SAIL), a framework designed to harness this speed without compromising rigor. By separating domain logic from code syntax, SAIL enables researchers to maintain strict oversight of scientific concepts and constraints while delegating code implementation to AI. We illustrate this approach through two open-source, browser-based astrophysics tools: an interactive gravitational lensing visualization and a large-scale structure formation sandbox, both publicly available. Our methodology condensed development to mere days while maintaining scientific integrity. We specifically address failure modes where AI-generated code neglects phenomenological boundaries or scientific validity. While cautioning that research-grade code requires stringent protocols, we demonstrate through two examples that SAIL provides an effective code generation workflow for outreach, teaching, professional presentations, and early-stage research prototyping. This framework contributes to a foundation for the further development of AI-assisted scientific software. |
| title | Setting SAIL: Leveraging Scientist-AI-Loops for Rigorous Visualization Tools |
| topic | Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Human-Computer Interaction |
| url | https://arxiv.org/abs/2603.18145 |