Artificial intelligence is rapidly transforming scientific workflows across domains. For example, in emerging autonomous laboratories (e.g., self-driving labs that autonomously synthesize and test new materials), artificial agents can design experiments, analyze results, and iteratively refine hypotheses within closed-loop pipelines, thus fundamentally transforming the role of scientists.

Such a transition creates new requirements and opportunities. Visual analytics enables oversight and steering of autonomous processes, facilitates the inspection and refinement of machine-generated hypotheses, and supports effective human-AI collaboration in knowledge generation and scientific discovery. This workshop positions visual analytics as a core enabler of autonomous scientific discovery and advances two complementary directions:

1. Developing interactive visual interfaces that support AI-accelerated science, and
2. Leveraging AI-accelerated scientific platforms to advance visualization research into AI-driven workflows.

We encourage submissions at the intersection of visual analytics and AI-driven scientific discovery across diverse application domains (e.g., materials science). We view the autonomous lab as a workflow and welcome contributions that use or advance visual analytics to support autonomous science across the entire pipeline or within specific stages of scientific discovery.

Goals

Our workshop aims to accomplish the following:

• Identify research opportunities within VIS for autonomous laboratories
• Share early-stage research, systems, applications, workflows, and ideas
• Build a cross-disciplinary autonomous labs research community centered around VIS
• Foster new connections between researchers and scientific domains

Topics

Our topics of interest include, but are not limited to, the following:

• Visualization, visual analytics, or immersive analytics for autonomous scientific discovery
• Evaluation of methods for designing, monitoring, and steering autonomous scientific processes
• Guidelines, strategies, and workflows for human-AI collaboration for scientific discovery and knowledge generation from multi-modal data
• Explainability, interpretability, and transparency of autonomous systems
• Uncertainty, trust, and reliability in AI-driven science
• Orchestration and visualization of multi-agent systems
• Provenance, reproducibility, and auditability
• Scalable and real-time visual analytics
• Ethical and safety considerations of autonomous science and the role of visual analytics in mitigating risks
• Case studies from application domains (e.g., physics, chemistry, materials science, drug discovery, biology, etc.)
• Use cases of leveraging AI to accelerate the science of visualization and analytic reasoning (e.g., adaptive user studies)
• The hand-off between human scientists and AI scientists in closed-loop workflows