arxiv
PublishedJuly 20, 2026 at 4:00 AM
HiLSVA: Design and Evaluation of a Human-in-the-Loop Agentic System for Scientific Visualization
Publisher summary· verbatim
arXiv:2606.26614v2 Announce Type: replace-cross Abstract: Large language model (LLM) agents enable natural language interaction for scientific visualization (SciVis). Still, prior systems have essentially prioritized autonomy over human analytical control, thereby limiting transparency and human ove
Stay posted· Newsletter
A 5-min weekly brief — top movers, price watch, story of the week.
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivCapacity and Redundancy Trade-offs in Multi-Task Learning12harxivPredictive Training with Latent Imagination for Visual Quadruped Navigation12harxivWhere Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making12harxivDid We Actually Fix It? An Independent Adversarial Stress-Test of Post-Point-Adjustment Evaluation Metrics for Time-Series Anomaly Detection12hThe Bubble Brief
WEEKLYRead AI insights every Tuesday — top movers, new releases, story of the week.
Originally published on arxiv ↗