Uncertainty visualizations, such as hurricane cones and ensemble tracks, are essential for risk communication but are often misinterpreted, leading to harmful decisions. As AI assistants like large language models (LLMs) increasingly support understanding of graphics and decision-making, they offer a promising pathway to enhance the interpretation of complex visualizations and a new opportunity to examine and improve the interpretation of uncertainty. We introduce UnReason, the first benchmark that systematically compares how humans and LLMs reason about hurricane forecast uncertainty visualizations. UnReason spans two escalating phases, seven representative visualization formats, six real hurricane cases, and three agent types (humans, LLMs with context, and LLMs without context), including 880 visualizations and 117,600 structured question–answer pairs under matched evaluation conditions. Phase 1 evaluates reasoning across implicit and explicit uncertainty encodings; Phase 2 examines reasoning under single- versus multi-dimensional uncertainty representations. We thoroughly assess damage estimation, reasoning strategies, and comprehension patterns, revealing that LLMs have a stronger semantic and conceptual understanding of uncertainty, and are less misled by visual variability, but still replicate key human biases during decision-making. Our findings offer insights into aligning LLM behavior with human cognition in uncertainty-rich visual reasoning tasks.

Effectively communicating uncertainty in ensemble hurricane forecasts poses a significant multimedia challenge, requiring the integration of spatial, temporal, and perceptual dimensions. We introduce SUVIS, a stereoscopic visualization system that encodes forecast ensembles into an immersive, layered media experience. SUVIS transforms multidimensional ensemble data into animated stereoscopic representations, mapping time to vertical depth, intensity to texture color, and forward speed to motion flow, while semi-transparent glyphs represent evolving impact areas. A progressive sampling strategy ensures spatial clarity across depth layers. Rendered on a glasses-free stereoscopic display, SUVIS frames uncertainty visualization as a media encoding problem, synthesizing motion, depth, and spatial abstraction to align with human perception. A user study with 51 participants demonstrates that SUVIS supports high accuracy in spatial tasks and enables interpretation of dynamic storm attributes. These results highlight the system's potential to advance perceptual uncertainty communication through multimedia representation and immersive visual encoding.

The operation and maintenance (O&M) of the communication network supporting a power system are essential for ensuring grid reliability. This paper presents VisualNetO&M, a collaborative visualization system integrated with a digital process twin of the communication network to enhance O&M efficiency. It provides visualizations for key tasks and facilitates collaboration among operators, technicians, and managers. We validated its effectiveness in Xi’an City, China, where it reduced the O&M workflow completion time from 16 hours to just 1 hour. This improvement resulted in a significant economic benefit of nearly 2/3 million USD over 10 months, highlighting the value of VisualNetO&M.

We introduce an interactive music system powered by AI-generated content (AIGC) that enables users to engage with music through multimodal interactions involving touch, sound, and spatial immersion. Motivated by the desire to enhance engagement and emotional connection with music, our system enables users to co-create and interact with musical content. Users upload a song and a descriptive text prompt, from which the system generates 3D visuals. During playback, users can embed their own audio inputs and trigger responsive visual effects such as color-driven point clouds using tangible controls. To explore how spatial scale and embodiment shape user experience, we implement the system across three increasing spatial scales and embodiments: (1) a handheld AR music box, (2) a table-sized stage box, and (3) a fully immersive VR environment. Through a user study, we investigate how different levels of immersion and interaction influence user engagement, emotional response, and sense of presence. Our findings demonstrate the potential of combining AIGC with embodied interaction to enrich creative expression and enhance immersive musical experiences.

Human-swarm interaction (HSI) is critical for scalable control of UAV swarm systems. Traditional interfaces struggle with generalization and user workload, especially in immersive environments. Hence, we present ChatHSI, a framework leveraging large language models (LLMs) for swarm task planning. ChatHSI integrates prompt engineering, action validation, and a human-in-the-loop mechanism to improve planning feasibility and executability. We implement ChatHSI in an immersive simulation to improve users’ spatial and situational awareness. Our method shows improved task efficiency, reduced workload, and higher usability in user studies. Ablation study proves the effectiveness of prompt context and action validation. The results show the feasibility of LLM-driven interaction for immersive swarm control and point toward adaptive, intuitive, and scalable HSI systems.

Abstract Comprehending visualizations requires readers to interpret visual encoding and the underlying meanings actively. This poses challenges for visualization novices, particularly when interpreting distributional visualizations that depict statistical uncertainty. Advancements in LLM‐based conversational interfaces show promise in promoting visualization comprehension. However, they fail to provide contextual explanations at fine‐grained granularity, and chart readers are still required to mentally bridge visual information and textual explanations during conversations. Our formative study highlights the expectations for both lexical and visual feedback, as well as the importance of explicitly linking these two modalities throughout the conversation. The findings motivate the design of VizTA, a visualization teaching assistant that leverages the fusion of visual and lexical feedback to help readers better comprehend visualization. VizTA features a semantic‐aware conversational agent capable of explaining contextual information within visualizations and employs a visual‐lexical fusion design to facilitate chart‐centered conversation. A between‐subject study with 24 participants demonstrates the effectiveness of VizTA in supporting the understanding and reasoning tasks of distributional visualization across multiple scenarios.

Animated hand-drawing sketches are a common way to communicate concepts and information. Sketches are also used to query charts, interact with visualizations, or express rough designs. However, there is little work investigating how people manually create visualization sketches and whether animated sketches can help users understand charts. We first conduct a user study that collects the sketching processes of people with visualization knowledge and then summarize the patterns in the sketch order. Based on the sketch patterns, we conduct a between-subject study to evaluate whether animated sketches can improve users’ performance of visualization tasks. We discuss the results of the user study and future work on evaluating the effectiveness of animated sketches.

Multiclass contour visualization is often used to interpret complex data attributes in such fields as weather forecasting, computational fluid dynamics, and artificial intelligence. However, effective and accurate representations of underlying data patterns and correlations can be challenging in multiclass contour visualization, primarily due to the inevitable visual cluttering and occlusions when the number of classes is significant. To address this issue, visualization design must carefully choose design parameters to make visualization more comprehensible. With this goal in mind, we proposed a framework for multiclass contour visualization. The framework has two components: a set of four visualization design parameters, which are developed based on an extensive review of literature on contour visualization, and a declarative domain-specific language (DSL) for creating multiclass contour rendering, which enables a fast exploration of those design parameters. A task-oriented user study was conducted to assess how those design parameters affect users' interpretations of real-world data. The study results offered some suggestions on the value choices of design parameters in multiclass contour visualization.