arXiv subjects

Fumeng Yang

Publications and source records attributed to Fumeng Yang.

12 recordsLinked to original sources

arXiv2026

What Do We Mean When We Talk About Infographics?

There has been limited clarity and consistency regarding what the term infographics, or information graphics, refers to in visualization research and practice. In particular, little is understood about where people's conceptualizations of infographics converge or diverge. To address this gap, we conducted a systematic literature review and a practitioner survey to identify and contrast different perspectives on infographics. We performed inductive coding on 487 sentences from 111 visualization and HCI papers, and analyzed questionnaire responses from 44 domain practitioners. Our findings reveal recurring dimensions of conceptual disagreement on infographics: role of text, relationship with data visualization, and relationships with statistical charts and data comics. Based on the results, we recommend future work to explicitly report any assumptions made along these diverging conceptualizations, to investigate the cognitive origins of these disagreements, and to develop more holistic design component frameworks for infographics. The supplementary materials are available via OSF and as an interactive web application.

cs.HC↗

arXiv2026

"Code Is Cheap. Show Me the Talk.": Lessons from Teaching and Managing AI Coding Tool Usage in a Visualization Course

Generative Artificial Intelligence (GenAI) coding tools are transforming visualization education. They can assist with implementation and design, but they can also let students bypass intended learning trajectories. In this paper, we share our retrospective experience managing and teaching AI use in an upper-level visualization course. We implemented prompt injections, asked oral checkout questions, and taught two AI coding labs. Prior to our coding labs, at least half of the students had already used AI tools in their assignments. In both AI coding labs, refinement accounted for about half of students' prompting logs, and explanation was almost absent. In the lab where AI coding was optional, 44 of 78 (56.4%) submissions preferred the scaffolded instructions over designing their own prompts. Students' final projects were more polished than in our previous offering, but also more visually homogeneous. Our reflections point to the need for clearer AI use boundaries and instruction on prompting, and for teaching students to question generic AI designs and adapt them to their data and story.

cs.HC↗

arXiv2026

Made to Feel: How Designers Bring Emotions into Affective Visualization

Affective visualization is increasingly studied in visualization research, yet how designers bring emotions into their visualization work remains unexplored. This paper addresses this gap through semi-structured interviews with 15 visualization practitioners. Using hybrid thematic analysis, we identify: (1) three functions that emotions can serve for viewers (entry, engagement, outcome); (2) three facets of how designers work with emotion (data, design, audience), along with design strategies; and (3) ethical considerations in the design process. We also observe that affective intent often emerges during the design process rather than being planned from the outset, and that emotional impact arises from accumulated design choices rather than isolated visual elements. Finally, we highlight evaluation as a key challenge identified by our participants.

cs.HC↗

arXiv2026

Reheat Nachos for Dinner? Evaluating AI Support for Cross-Cultural Communication of Neologisms

Neologisms and emerging slang are central to daily conversation, yet challenging for non-native speakers (NNS) to interpret and use appropriately in cross-cultural communication with native speakers (NS). NNS increasingly make use of Artificial Intelligence (AI) tools to learn these words. We study the utility of such tools in mediating an informal communication scenario through a human-subjects study (N=234): NNS participants learn English neologisms with AI support, write messages using the learned word to an NS friend, and judge contextual appropriateness of the neologism in two provided writing samples. Using both NS evaluator-rated communicative competence of NNS-produced writing and NNS' contextual appropriateness judgments, we compare three AI-based support conditions: AI Definition, AI Rewrite into simpler English, AI Explanation of meaning and usage, and Non-AI Dictionary for comparison. We show that AI Explanation yields the largest gains over no support in NS-rated competence, while contextual appropriateness judgments show indifference across support. NNS participants' self-reported perceptions tend to overestimate NS ratings, revealing a mismatch between perceived and actual competence. We further observe a significant gap between NNS- and NS-produced writing, highlighting the limitations of current AI tools and informing design for future tools.

cs.CL↗

arXiv2026

Trace-Aware Workflows for Co-Creating Branded Content with Generative AI

Generative AI tools have lowered barriers to producing branded social media images and captions, yet small-business owners (SBOs) still struggle to create on-brand posts without access to professional designers or marketing consultants. Although these tools enable fast image generation from text prompts, aligning outputs with a brand's intended look and feel remains a demanding, iterative task. In this position paper, we explore how SBOs navigate iterative content creation and how AI-assisted systems can support SBOs' content creation workflow. We conducted a preliminary study with 12 SBOs who independently manage their businesses and social media presence, using a questionnaire to collect their branding practices, content workflows, and use of generative AI alongside conventional design tools. We identified three recurring challenges: (1) translating brand "feel" into effective prompts, (2) difficulty revisiting and comparing prior image generations, and (3) difficulty making sense of changes between iterations to steer refinement. Based on these findings, we present a prototype that scaffolds brand articulation, supports feedback-informed exploration, and maintains a traceboard of branching image iterations. Our work illustrates how traces of the iterative process can serve as workflow support that helps SBOs keep track of explorations, make sense of changes, and refine content.

cs.HC↗

arXiv2026

Codesigning Ripplet: an LLM-Assisted Assessment Authoring System Grounded in a Conceptual Model of Teachers' Workflows

Assessments are critical in education, but creating them can be difficult. To address this challenge in a grounded way, we partnered with 13 teachers in a seven-month codesign process. We developed a conceptual model that characterizes the iterative dual process where teachers develop assessments while simultaneously refining requirements. To enact this model in practice, we built Ripplet, a web-based tool with multilevel reusable interactions to support assessment authoring. The extended codesign revealed that Ripplet enabled teachers to create formative assessments they would not have otherwise made, shifted their practices from generation to curation, and helped them reflect more on assessment quality. In a user study with 15 additional teachers, compared to their current practices, teachers felt the results were more worth their effort and that assessment quality improved.

cs.HC↗

arXiv2025

DuoZone: A User-Centric, LLM-Guided Mixed-Initiative XR Window Management System

Mixed reality (XR) environments offer vast spatial possibilities, but current window management systems require users to manually place, resize, and organize multiple applications across large 3D spaces. This creates cognitive and interaction burdens that limit productivity. We introduce DuoZone, a mixed-initiative XR window management system that combines user-defined spatial layouts with LLM-guided automation. DuoZone separates window management into two complementary zones. The Recommendation Zone enables fast setup by providing spatial layout templates and automatically recommending relevant applications based on user tasks and high-level goals expressed through voice or text. The Arrangement Zone supports precise refinement through direct manipulation, allowing users to adjust windows using natural spatial actions such as dragging, resizing, and snapping. Through this dual-zone approach, DuoZone promotes efficient organization while reducing user cognitive load. We conducted a user study comparing DuoZone with a baseline manual XR window manager. Results show that DuoZone improves task completion speed, reduces mental effort, and increases sense of control when working with multiple applications in XR. We discuss design implications for future mixed-initiative systems and outline opportunities for integrating adaptive, goal-aware intelligence into spatial computing workflows.

cs.HC↗

arXiv2025

A Good Plan is Hard to Find: Aligning Models with Preferences is Misaligned with What Helps Users

To assist users in complex tasks, LLMs generate plans: step-by-step instructions towards a goal. While alignment methods aim to ensure LLM plans are helpful, they train (RLHF) or evaluate (ChatbotArena) on what users prefer, assuming this reflects what helps them. We test this with Planorama: an interface where 126 users answer 300 multi-step questions with LLM plans. We get 4388 plan executions and 5584 comparisons to measure plan helpfulness (QA success) and user preferences on plans, and recreate the setup in agents and reward models to see if they simulate or prefer what helps users. We expose: 1) user/model preferences and agent success do not accurately predict which plans help users, so common alignment feedback can misalign with helpfulness; 2) this gap is not due to user-specific preferences, as users are similarly successful when using plans they prefer/disprefer; 3) surface-level cues like brevity and question similarity strongly link to preferences, but such biases fail to predict helpfulness. In all, we argue aligning helpful LLMs needs feedback from real user interactions, not just preferences of what looks helpful, so we discuss the plan NLP researchers can execute to solve this problem.

cs.CL↗

arXiv2025

Self-Supervised Continuous Colormap Recovery from a 2D Scalar Field Visualization without a Legend

Recovering a continuous colormap from a single 2D scalar field visualization can be quite challenging, especially in the absence of a corresponding color legend. In this paper, we propose a novel colormap recovery approach that extracts the colormap from a color-encoded 2D scalar field visualization by simultaneously predicting the colormap and underlying data using a decoupling-and-reconstruction strategy. Our approach first separates the input visualization into colormap and data using a decoupling module, then reconstructs the visualization with a differentiable color-mapping module. To guide this process, we design a reconstruction loss between the input and reconstructed visualizations, which serves both as a constraint to ensure strong correlation between colormap and data during training, and as a self-supervised optimizer for fine-tuning the predicted colormap of unseen visualizations during inferencing. To ensure smoothness and correct color ordering in the extracted colormap, we introduce a compact colormap representation using cubic B-spline curves and an associated color order loss. We evaluate our method quantitatively and qualitatively on a synthetic dataset and a collection of real-world visualizations from the VIS30K dataset. Additionally, we demonstrate its utility in two prototype applications -- colormap adjustment and colormap transfer -- and explore its generalization to visualizations with color legends and ones encoded using discrete color palettes.

cs.CV↗

arXiv2025

Whose Boat Does it Float? Improving Personalization in Preference Tuning via Inferred User Personas

LLMs are aligned to follow input instructions by learning which of two responses users prefer for a prompt. However, such preference data do not convey why users prefer responses that are chosen or rejected, so LLMs trained on these datasets cannot tailor responses to varied user needs. To surface these parameters of personalization, we apply abductive reasoning to preference data, inferring needs and interests of users, i.e., personas, that may prefer either response. We test this idea in two steps: Persona Inference (PI), abductively inferring personas of users who prefer chosen or rejected outputs, and Persona Tailoring (PT), training models to tailor outputs to personas from PI. We show: 1) LLMs infer personas accurately explaining why different users may prefer both chosen or rejected outputs; 2) Training on preference data augmented with PI personas via PT boosts personalization and generalizes to supporting user-written personas; and 3) Rejected response personas form harder personalization evaluations, showing PT better aids users with uncommon preferences versus typical alignment methods. We argue for an abductive view of preferences for personalization, asking not only which response is better but when, why, and for whom.

cs.CL↗

arXiv2023

Adaptive Assessment of Visualization Literacy

Visualization literacy is an essential skill for accurately interpreting data to inform critical decisions. Consequently, it is vital to understand the evolution of this ability and devise targeted interventions to enhance it, requiring concise and repeatable assessments of visualization literacy for individuals. However, current assessments, such as the Visualization Literacy Assessment Test (VLAT), are time-consuming due to their fixed, lengthy format. To address this limitation, we develop two streamlined computerized adaptive tests (CATs) for visualization literacy, A-VLAT and A-CALVI, which measure the same set of skills as their original versions in half the number of questions. Specifically, we (1) employ item response theory (IRT) and non-psychometric constraints to construct adaptive versions of the assessments, (2) finalize the configurations of adaptation through simulation, (3) refine the composition of test items of A-CALVI via a qualitative study, and (4) demonstrate the test-retest reliability (ICC: 0.98 and 0.98) and convergent validity (correlation: 0.81 and 0.66) of both CATs via four online studies. We discuss practical recommendations for using our CATs and opportunities for further customization to leverage the full potential of adaptive assessments. All supplemental materials are available at https://osf.io/a6258/.

cs.HC↗

arXiv2021

Rethinking the Ranks of Visual Channels

Data can be visually represented using visual channels like position, length or luminance. An existing ranking of these visual channels is based on how accurately participants could report the ratio between two depicted values. There is an assumption that this ranking should hold for different tasks and for different numbers of marks. However, there is little existing work testing assumption, especially given that visually computing ratios is relatively unimportant in real-world visualizations, compared to seeing, remembering, and comparing trends and motifs, across displays that almost universally depict more than two values. We asked participants to immediately reproduce a set of values from memory. With a Bayesian multilevel modeling approach, we observed how the relevant rank positions of visual channels shift across different numbers of marks (2, 4 or 8) and for bias, precision, and error measures. The ranking did not hold, even for reproductions of only 2 marks, and the new ranking was highly inconsistent for reproductions of different numbers of marks. Other factors besides channel choice far more influence on performance, such as the number of values in the series (e.g. more marks led to larger errors), or the value of each mark (e.g. small values are systematically overestimated). Recall was worse for displays with 8 marks than 4, consistent with established limits on visual memory. These results show that we must move beyond two-value ratio judgments as a baseline for ranking the quality of a visual channel, including testing new tasks (detection of trends or motifs), timescales (immediate computation, or later comparison), and the number of values (from a handful, to thousands).

cs.HC↗