HiLSVA: Design and Evaluation of a Human-in-the-Loop Agentic System for Scientific Visualization
arXiv:2606.26614v1 Announce Type: 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 oversight. We present HiLSVA, a human-in-the-loop agentic system that supports mixed-initiative SciVis workflows. HiLSVA integrates a plan-first multi-agent architecture with explicit human oversight, stepwise provenance tracking, and learn-at-test-time adaptation from user feedback. The system supports fluid
The rapid advancement and adoption of large language models are pushing the boundaries of autonomous systems, leading to a critical need for balanced human oversight in complex applications like scientific visualization.
Maintaining human analytical control and transparency in AI-driven scientific visualization is crucial for validating results, fostering trust, and preventing autonomous systems from introducing unverified biases or errors into research.
The paradigm for human-AI interaction in scientific visualization shifts from prioritizing AI autonomy to integrating explicit human-in-the-loop oversight, enhancing reliability and interpretability.
- · Scientific researchers
- · AI ethicists
- · Data visualization tool developers
- · Industries relying on complex data analysis
- · Developers of fully autonomous AI visualization tools
- · Organizations with low transparency requirements
Increased adoption of hybrid human-AI systems across various analytical domains requiring high assurance.
Development of new regulatory frameworks and best practices for human-in-the-loop AI systems to ensure accountability and control.
Enhanced scientific discovery and validation processes due to more reliable and interpretable AI-assisted visualization, potentially accelerating breakthroughs.
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Read at arXiv cs.AI