Multimodal deep-learning models power interactive video retrieval by ranking keyframes in response to textual queries. Despite these advances, users must still browse ranked candidates manually to locate a target. Keyframe arrangement within the search grid highly affects browsing effectiveness and user efficiency, yet remains underexplored. We report a study with 49 participants evaluating seven keyframe layouts for the Visual Known-Item Search task. Beyond efficiency and accuracy, we relate browsing phenomena, such as overlooks, to layout characteristics. Our results show that a video-grouped layout is the most efficient, while a four-column, rank-preserving grid achieves the highest accuracy. Sorted grids reveal potential and trade-offs, enabling rapid scanning of uninteresting regions but down-ranking relevant targets to less prominent positions, delaying first arrival times and increasing overlooks. These findings motivate hybrid designs that preserve positions of top-ranked items while sorting or grouping the remainder, and offer guidance for searching in grids beyond video retrieval.

The creation of systematic literature reviews (SLR) is critical for analyzing the landscape of a research field and guiding future research directions. However, retrieving and filtering the literature corpus for an SLR is highly time-consuming and requires extensive manual effort, as keyword-based searches in digital libraries often return numerous irrelevant publications. In this work, we propose a pipeline leveraging multiple large language models (LLMs), classifying papers based on descriptive prompts and deciding jointly using a consensus scheme. The entire process is human-supervised and interactively controlled via our open-source visual analytics web interface, LLMSurver, which enables real-time inspection and modification of model outputs. We evaluate our approach using ground-truth data from a recent SLR comprising 8323 candidate papers, benchmarking both open and commercial state-of-the-art LLMs from mid-2024 and fall 2025. Results demonstrate that our pipeline significantly reduces manual effort while achieving lower error rates than single human annotators. Furthermore, modern open-source models prove sufficient for this task, making the method accessible and cost-effective. Overall, our work demonstrates how responsible human-AI collaboration can accelerate and enhance systematic literature reviews within academic workflows. • A new pipeline for semi-automated literature review. • Large language models vote using a consensus scheme. • User keeps the control through a visual analytics approach. • Evaluation with previous and modern state-of-the-art models (open and commercial). • Available online demo tool and open-source code.

The shifting and ever-growing demand for energy, for instance, driven by transformations towards new technologies such as electric vehicles, heat pumps, battery storage, or rooftop solar, requires urban infrastructure to adapt. Upgrading legacy infrastructure, such as undersized electric cables, is costly, time-consuming, and disruptive, and therefore requires a holistic perspective and thorough urban planning that considers multi energy systems and co-located utilities. We present EnMRgy, a mixed-reality decision-support system that enables experts and decision-makers to explore a city’s energy distribution networks, together with demand simulations and scenarios for infrastructure development. Within an immersive 3D city context, an energy network such as a power grid, modelled as a weighted graph, is visualised. Interactive functionalities allow users to adjust visual representations and compare scenarios across three different views. Our work enables evidence-based strategic planning for future-ready energy networks.

Deep learning has revolutionized multimedia retrieval, yet effectively searching within large video collections remains a complex challenge. This paper focuses on the design and evaluation of known-item search systems, leveraging the strengths of CLIP-based deep neural networks for ranking. At events like the Video Browser Showdown, these models have shown promise in effectively ranking the video frames. While ranking models can be pre-selected automatically based on a benchmark collection, the selection of an optimal browsing interface, crucial for refining top-ranked items, is complex and heavily influenced by user behavior. Our study addresses this by presenting an eye tracking-based analysis of user interaction with different image grid layouts. This approach offers novel insights into search patterns and user preferences, particularly examining the trade-off between displaying fewer but larger images versus more but smaller images. Our findings reveal a preference for grids with fewer images and detail how image similarity and grid position affect user search behavior. These results not only enhance our understanding of effective video retrieval interface design but also set the stage for future advancements in the field.

The visual exploration of trajectory data is crucial in domains such as animal behavior, molecular dynamics, and transportation. With the emergence of immersive technology, trajectory data, which is often inherently three-dimensional, can be analyzed in stereoscopic 3D, providing new opportunities for perception, engagement, and understanding. However, the interaction with the presented data remains a key challenge. While most applications depend on hand tracking, we see eye tracking as a promising yet under-explored interaction modality, while challenges such as imprecision or inadvertently triggered actions need to be addressed. In this work, we explore the potential of eye gaze interaction for the visual exploration of trajectory data within an AR environment. We integrate hand- and eye-based interaction techniques specifically designed for three common use cases and address known eye tracking challenges. We refine our techniques and setup based on a pilot user study (n=6) and find in a follow-up study (n=20) that gaze interaction can compete with hand-tracked interaction regarding effectiveness, efficiency, and task load for selection and cluster exploration tasks. However, time step analysis comes with higher answer times and task load. In general, we find the results and preferences to be user-dependent. Our work contributes to the field of immersive data exploration, underscoring the need for continued research on eye tracking interaction.

Abstract Understanding the behavior of deep reinforcement learning agents is a crucial requirement throughout their development. Existing work has addressed the identification of observable behavioral patterns in state sequences or analysis of isolated internal representations; however, the overall decision‐making of deep‐learning RL agents remains opaque. To tackle this, we present VISITOR, a visual analytics system enabling the analysis of entire state sequences, the diagnosis of singular predictions, and the comparison between agents. A sequence embedding view enables the multiscale analysis of state sequences, utilizing custom embedding techniques for a stable spatialization of the observations and internal states. We provide multiple layers: (1) a state space embedding, highlighting different groups of states inside the state‐action sequences, (2) a trajectory view, emphasizing decision points, (3) a network activation mapping, visualizing the relationship between observations and network activations, (4) a transition embedding, enabling the analysis of state‐to‐state transitions. The embedding view is accompanied by an interactive reward view that captures the temporal development of metrics, which can be linked directly to states in the embedding. Lastly, a model list allows for the quick comparison of models across multiple metrics. Annotations can be exported to communicate results to different audiences. Our two‐stage evaluation with eight experts confirms the effectiveness in identifying states of interest, comparing the quality of policies, and reasoning about the internal decision‐making processes.

Networks are an important means for the representation and analysis of data in a variety of research and application areas. While there are many efficient methods to create layouts for networks to support their visual analysis, approaches for the comparison of networks are still underexplored. Especially when it comes to the comparison of weighted networks, which is an important task in several areas, such as biology and biomedicine, there is a lack of efficient visualization approaches. With the availability of affordable high-quality virtual reality (VR) devices, such as head-mounted displays (HMDs), the research field of immersive analytics emerged and showed great potential for using the new technology for visual data exploration. However, the use of immersive technology for the comparison of networks is still underexplored. With this work, we explore how weighted networks can be visually compared in an immersive VR environment and investigate how visual representations can benefit from the extended 3D design space. For this purpose, we develop different encodings for 3D node-link diagrams supporting the visualization of two networks within a single representation and evaluate them in a pilot user study. We incorporate the results into a more extensive user study comparing node-link representations with matrix representations encoding two networks simultaneously. The data and tasks designed for our experiments are similar to those occurring in real-world scenarios. Our evaluation shows significantly better results for the node-link representations, which is contrary to comparable 2D experiments and indicates a high potential for using VR for the visual comparison of networks.

Analysing and understanding brain function and disorder is the main focus of neuroscience. Due to the high complexity of the brain, directionality of the signal and changing activity over time, visual exploration and data analysis are difficult. For this reason, a vast amount of research challenges are still unsolved. We explored different challenges of the visual analysis of brain data and the design of corresponding immersive environments in collaboration with experts from the biomedical domain. We built a prototype of an immersive virtual reality environment to explore the design space and to investigate how brain data analysis can be supported by a variety of design choices. Our environment can be used to study the effect of different visualisations and combinations of brain data representation, as for example network layouts, anatomical mapping or time series. As a long-term goal, we aim to aid neuro-scientists in a better understanding of brain function and disorder.