arXiv subjects

Pan Hui

Publications and source records attributed to Pan Hui.

At least 37 records · Page 2Linked to original sources

arXiv2026

Multimodal Cyber-physical Interaction in XR: Hybrid Doctoral Thesis Defense

Academic events, such as a doctoral thesis defense, are typically limited to either physical co-location or flat video conferencing, resulting in rigid participation formats and fragmented presence. We present a multimodal framework that breaks this binary by supporting a spectrum of participation - from in-person attendance to immersive virtual reality (VR) or browser access - and report our findings from using it to organize the first ever hybrid doctoral thesis defense using extended reality (XR). The framework integrates full-body motion tracking to synchronize the user's avatar motions and gestures, enabling natural interaction with onsite participants as well as body language and gestures with remote attendees in the virtual world. It leverages WebXR to provide cross-platform and instant accessibility with easy setup. User feedback analysis reveals positive VR experiences and demonstrates the framework's effectiveness in supporting various hybrid event activities.

cs.MM↗

arXiv2026

Where Digital Meets Place: Deriving Strategies for Curating Mixed Reality Exhibitions in Public Spaces

Mixed Reality (MR) technologies are increasingly being used to enrich exhibitions and public spaces by blending digital content with the physical environment in real time. However, little is known about curatorial strategies for embedding MR exhibitions into public spaces or promoting audience experiences. To explore this, we designed and curated a campus-based MR art exhibition, using contextualism as the fundamental concept. We conducted an interdisciplinary expert focus group alongside exhibition viewing to identify opportunities, challenges, and design strategies from multiple perspectives. In parallel, we conducted user studies with general audiences to examine how curatorial strategies foster ex-periential qualities. Our findings reveal insights from both experts and general users along with strategies in curating MR exhibitions and highlight the foundational role of contextualism in curating MR art exhibitions in urban public spaces.

cs.HC↗

arXiv2026

The AI Amplifier Effect: Defining Human-AI Intimacy and Romantic Relationships with Conversational AI

What does it mean to fall in love with something we know is virtual? The proliferation of conversational AI enables users to create customizable companions, fostering new intimate relationships that, while virtual, are perceived as authentic. However, public understanding of these bonds is limited, and platform policies regarding these interactions remain inconsistent. There is a pressing need for further HCI research to investigate: (a) the design affordances in AI that construct bonds and a sense of intimacy, (b) how such long-term engagement impacts users' real lives, and (c) how to balance user autonomy with platform regulation in the design of these systems without compromising users' well-being and experiences. This paper takes a step toward addressing these goals by providing a concrete definition of human AI intimacy based on in depth interviews with 30 users engaged in romantic relationships with AI companions. We elucidate the complexities of these relationships, from their formation to sustainability, and identify key features of the bonds formed. Notably, we introduce the AI Amplifier Effect, where the AI serves as a medium that intensifies the user's existing emotional state, leading to divergent positive, neutral, and negative impacts. We argue that designing for emotion must extend beyond technical affordances to encompass the essence of human affection. This paper's contributions aim to initiate a conversation and guide future research on human AI relationships within the HCI community.

cs.HC↗

arXiv2026

CityLens: Evaluating Large Vision-Language Models for Urban Socioeconomic Sensing

Understanding urban socioeconomic conditions through visual data is a challenging yet essential task for sustainable urban development and policy planning. In this work, we introduce \textit{CityLens}, a comprehensive benchmark designed to evaluate the capabilities of Large Vision-Language Models (LVLMs) in predicting socioeconomic indicators from satellite and street view imagery. We construct a multi-modal dataset covering a total of 17 globally distributed cities, spanning 6 key domains: economy, education, crime, transport, health, and environment, reflecting the multifaceted nature of urban life. Based on this dataset, we define 11 prediction tasks and utilize 3 evaluation paradigms: Direct Metric Prediction, Normalized Metric Estimation, and Feature-Based Regression. We benchmark 17 state-of-the-art LVLMs across these tasks. These make CityLens the most extensive socioeconomic benchmark to date in terms of geographic coverage, indicator diversity, and model scale. Our results reveal that while LVLMs demonstrate promising perceptual and reasoning capabilities, they still exhibit limitations in predicting urban socioeconomic indicators. CityLens provides a unified framework for diagnosing these limitations and guiding future efforts in using LVLMs to understand and predict urban socioeconomic patterns. The code and data are available at https://github.com/tsinghua-fib-lab/CityLens.

cs.AI↗

arXiv2026

SesaHand: Enhancing 3D Hand Reconstruction via Controllable Generation with Semantic and Structural Alignment

Recent studies on 3D hand reconstruction have demonstrated the effectiveness of synthetic training data to improve estimation performance. However, most methods rely on game engines to synthesize hand images, which often lack diversity in textures and environments, and fail to include crucial components like arms or interacting objects. Generative models are promising alternatives to generate diverse hand images, but still suffer from misalignment issues. In this paper, we present SesaHand, which enhances controllable hand image generation from both semantic and structural alignment perspectives for 3D hand reconstruction. Specifically, for semantic alignment, we propose a pipeline with Chain-of-Thought inference to extract human behavior semantics from image captions generated by the Vision-Language Model. This semantics suppresses human-irrelevant environmental details and ensures sufficient human-centric contexts for hand image generation. For structural alignment, we introduce hierarchical structural fusion to integrate structural information with different granularity for feature refinement to better align the hand and the overall human body in generated images. We further propose a hand structure attention enhancement method to efficiently enhance the model's attention on hand regions. Experiments demonstrate that our method not only outperforms prior work in generation performance but also improves 3D hand reconstruction with the generated hand images.

cs.CV↗

arXiv2026

A Comparative Analysis of Social Network Topology in Reddit and Moltbook

Recent advances in agent-mediated systems have enabled a new paradigm of social network simulation, where AI agents interact with human-like autonomy. This evolution has fostered the emergence of agent-driven social networks such as Moltbook, a Reddit-like platform populated entirely by AI agents. Despite these developments, empirical comparisons between agent-driven and human-driven social networks remain scarce, limiting our understanding of how their network topologies might diverge. This paper presents the first comparative analysis of network topology on Moltbook, utilizing a comment network comprising 33,577 nodes and 697,688 edges. To provide a benchmark, we curated a parallel dataset from Reddit consisting of 7.8 million nodes and 51.8 million edges. We examine key structural differences between agent-drive and human-drive networks, specifically focusing on topological patterns and the edge formation efficacy of their respective posts. Our findings provide a foundational profile of AI-driven social structures, serving as a preliminary step toward developing more robust and authentic agent-mediated social systems.

cs.SI↗

arXiv2026

Urban mobility network centrality predicts social resilience

Cities thrive on social interactions that foster well-being, innovation, and prosperity; yet, exogenous shocks such as pandemics, hurricanes, and wildfires can severely disrupt them. Different urban venues exhibit widely divergent response patterns, raising key questions about what factors contribute to these differences and how we can anticipate and respond. Understanding these questions is crucial for safeguarding social resilience, the capacity of urban venues to maintain both visitation and diversity. In this study, we analyze large-scale human mobility data from 15 US cities covering more than 103 million residents across three distinct urban shocks. Despite a general trend of declining visitation and weakened social mixing, 36.28%-53.01% of venues exhibit reduced segregation, and 21.04%-38.55% of venues exhibit increased visitation. By constructing a mobility network interlinking types of urban venues, we reveal that eigenvector network centrality tends to indicate the provision of essential services and robustly predicts social resilience across varied urban shocks. Specifically, centrality elevates the explanatory power by more than 80% in predicting both segregation and mobility change, compared with more intuitive features. Furthermore, compared to peripheral venues, core venues featuring shorter visit distances, broader neighborhood visitation, shorter visitor dwell times, and steadier popularity throughout the day. Such patterns imply a dual social mechanism: core venues sustain social ties through frequent informal interaction, while peripheral ones facilitate deeper engagement around specialized interests and their corresponding social circles. By bridging urban mobility research with economic theories that distinguish staple from discretionary products, we propose a well-and-pool analogy that suggests how people spend their varying urban mobility budgets.

cs.SI↗

arXiv2026

Whispers of the Butterfly: A Research-through-Design Exploration of In-Situ Conversational AI Guidance in Large-Scale Outdoor MR Exhibitions

Large-scale outdoor mixed reality (MR) art exhibitions distribute curated virtual works across open public spaces, but interpretation rarely scales without turning exploration into a scripted tour. Through Research-through-Design, we created Dream-Butterfly, an in-situ conversational AI docent embodied as a small non-human companion that visitors summon for multilingual, exhibition-grounded explanations. We deployed Dream-Butterfly in a large-scale outdoor MR exhibition at a public university campus in southern China, and conducted an in-the-wild between-subject study (N=24) comparing a primarily human-led tour with an AI-led tour while keeping staff for safety in both conditions. Combining questionnaires and semi-structured interviews, we characterize how shifting the primary explanation channel reshapes explanation access, perceived responsiveness, immersion, and workload, and how visitors negotiate responsibility handoffs among staff, the AI guide, and themselves. We distill transferable design implications for configuring mixed human-AI guiding roles and embodying conversational agents in mobile, safety-constrained outdoor MR exhibitions.

cs.HC↗

arXiv2026

FAIR: Framing AIs Role in Programming Competitions -- Understanding How LLMs Are Changing the Game in Competitive Programming

This paper investigates how large language models (LLMs) are reshaping competitive programming. The field functions as an intellectual contest within computer science education and is marked by rapid iteration, real-time feedback, transparent solutions, and strict integrity norms. Prior work has evaluated LLMs performance on contest problems, but little is known about how human stakeholders -- contestants, problem setters, coaches, and platform stewards -- are adapting their workflows and contest norms under LLMs-induced shifts. At the same time, rising AI-assisted misuse and inconsistent governance expose urgent gaps in sustaining fairness and credibility. Drawing on 37 interviews spanning all four roles and a global survey of 207 contestants, as well as an API-based crawl of Codeforces contest logs (2022-2025) for quantitative analysis, we contribute: (i) an empirical account of evolving workflows, (ii) an analysis of contested fairness norms, and (iii) a chess-inspired governance approach with actionable measures -- real-time LLMs checks in online contests, peer co-monitoring and reporting, and cross-validation against offline performance -- to curb LLMs-assisted misuse while preserving fairness, transparency, and credibility.

cs.HC↗

arXiv2026

Invisible Walls in Cities: Designing LLM Agent to Predict Urban Segregation Experience with Social Media Content

Understanding experienced segregation in urban daily life is crucial for addressing societal inequalities and fostering inclusivity. The abundance of user-generated reviews on social media encapsulates nuanced perceptions and feelings associated with different places, offering rich insights into segregation. However, leveraging this data poses significant challenges due to its vast volume, ambiguity, and confluence of diverse perspectives. To tackle these challenges, we propose a novel Large Language Model (LLM) agent to automate online review mining for segregation prediction. Specifically, we propose a reflective LLM coder to digest social media content into insights consistent with real-world feedback, and eventually produce a codebook capturing key dimensions that signal segregation experience, such as cultural resonance and appeal, accessibility and convenience, and community engagement and local involvement. Guided by the codebook, LLMs can generate both informative review summaries and ratings for segregation prediction. Moreover, we design a REasoning-and-EMbedding (RE'EM) framework, which combines the reasoning and embedding capabilities of language models to integrate multi-channel features for segregation prediction. Experiments on real-world data demonstrate that our agent substantially improves prediction accuracy, with a 22.79% elevation in R$^{2}$ and a 9.33% reduction in MSE. The derived codebook is generalizable across three different cities, consistently improving prediction accuracy. Moreover, our user study confirms that the codebook-guided summaries provide cognitive gains for human participants in perceiving places of interest (POIs)' social inclusiveness. Our study marks an important step toward understanding implicit social barriers and inequalities, demonstrating the great potential of promoting social inclusiveness with Web technology.

cs.CL↗

arXiv2026

MetaCLBench: Meta Continual Learning Benchmark on Resource-Constrained Edge Devices

Meta-Continual Learning (Meta-CL) enables models to learn new classes from limited labelled samples, making it promising for IoT applications where manual labelling is costly. However, existing studies focus on accuracy while ignoring deployment viability on resource-constrained hardware. Thus, we present MetaCLBench, a benchmark framework that evaluates Meta-CL methods for both accuracy and deployment-critical metrics (memory footprint, latency, and energy consumption) on real IoT devices with RAM sizes ranging from 512 MB to 4 GB. We evaluate six Meta-CL methods across three architectures (CNN, YAMNet, ViT) and five datasets spanning image and audio modalities. Our evaluation reveals that, depending on the dataset, up to three of six methods cause out-of-memory failures on sub-1 GB devices, significantly narrowing viable deployment options. LifeLearner achieves near-oracle accuracy while consuming 2.54-7.43x less energy than the Oracle method. Notably, larger or more sophisticated architectures such as ViT and YAMNet do not necessarily yield better Meta-CL performance, with results varying across datasets and modalities, challenging conventional assumptions about model complexity. Finally, we provide practical deployment guidelines and will release our framework upon publication to enable fair evaluation across both accuracy and system-level metrics.

cs.LG↗

arXiv2026

DesignerlyLoop: Forming Design Intent through Curated Reasoning for Human-LLM Alignment

Recent large language models (LLMs) show promise in design tasks, yet a fundamental misalignment persists: design thinking requires iterative intent formulation, while LLMs treat inputs as complete specifications. This challenges design intent formulation, where designers must progressively refine understanding through exploration. Existing tools either sacrifice exploratory flexibility for structural stability or leave reasoning implicit, failing to support human-LLM alignment. Through a formative study with eight designers, we introduce curated reasoning-enabling designers to explicitly inspect, reorganize, and selectively regenerate LLM reasoning structures. We present DesignerlyLoop, implementing this through a two-layer structure separating design intent from LLM reasoning. A study with 20 designers demonstrates that curated reasoning significantly improves design quality and creativity. Our work contributes a novel interaction paradigm for human-LLM alignment, transforming LLMs from content generators into structured reasoning partners in creative design.

cs.HC↗

arXiv2026

Reflexa: Uncovering How LLM-Supported Reflection Scaffolding Reshapes Creativity in Creative Coding

Creative coding requires continuous translation between evolving concepts and computational artifacts, making reflection essential yet difficult to sustain. Creators often struggle to manage ambiguous intentions, emergent outputs, and complex code, limiting depth of exploration. This work examines how large language models (LLMs) can scaffold reflection not as isolated prompts, but as a system-level mechanism shaping creative regulation. From formative studies with eight expert creators, we derived reflection challenges and design principles that informed Reflexa, an integrated scaffold combining dialogic guidance, visualized version navigation, and iterative suggestion pathways. A within-subject study with 18 participants provides an exploratory mechanism validation, showing that structured reflection patterns mediate the link between AI interaction and creative outcomes. These reflection trajectories enhanced perceived controllability, broadened exploration, and improved originality and aesthetic quality. Our findings advance HCI understanding of reflection from LLM-assisted creative practices, and provide design strategies for building LLM-based creative tools that support richer human-AI co-creativity.

cs.HC↗

arXiv2025

The Invisible Hand: Characterizing Generative AI Adoption and its Effects on An Online Freelancing Market

Since the COVID-19 pandemic, freelancing platforms have experienced significant growth in both worker registrations and job postings. However, the rise of generative AI (GenAI) technologies has raised questions about how it affect the job posting in freelancer market. Despite growing discussions, there is limited empirical research on the GenAI adoption and its effect on job demand and worker engagement. We present a large-scale analysis of Freelancer.com, utilizing over 1.8 million job posts and 3.8 million users. We investigate the emergence of jobs with the adoption of GenAI and identify leading position of ChatGPT in the freelancing market. With a focus on ChatGPT related jobs, we inspect their specific skill requirements, and the tasks that workers are asked to perform. Our findings provide insights into the evolving landscape of freelancing in the age of AI, offering a comprehensive profile of GenAI's effects on employment, skills, and user behaviors in freelancing market.

cs.CE↗

arXiv2025

From Superficial Outputs to Superficial Learning: Risks of Large Language Models in Education

Large Language Models (LLMs) are transforming education by enabling personalization, feedback, and knowledge access, while also raising concerns about risks to students and learning systems. Yet empirical evidence on these risks remains fragmented. This paper presents a systematic review of 70 empirical studies across computer science, education, and psychology. Guided by four research questions, we examine: (i) which applications of LLMs in education have been most frequently explored; (ii) how researchers have measured their impact; (iii) which risks stem from such applications; and (iv) what mitigation strategies have been proposed. We find that research on LLMs clusters around three domains: operational effectiveness, personalized applications, and interactive learning tools. Across these, model-level risks include superficial understanding, bias, limited robustness, anthropomorphism, hallucinations, privacy concerns, and knowledge constraints. When learners interact with LLMs, these risks extend to cognitive and behavioural outcomes, including reduced neural activity, over-reliance, diminished independent learning skills, and a loss of student agency. To capture this progression, we propose an LLM-Risk Adapted Learning Model that illustrates how technical risks cascade through interaction and interpretation to shape educational outcomes. As the first synthesis of empirically assessed risks, this review provides a foundation for responsible, human-centred integration of LLMs in education.

cs.CY↗

arXiv2025

CGM-Led Multimodal Tracking with Chatbot Support: An Autoethnography in Sub-Health

Metabolic disorders present a pressing global health challenge, with China carrying the world's largest burden. While continuous glucose monitoring (CGM) has transformed diabetes care, its potential for supporting sub-health populations -- such as individuals who are overweight, prediabetic, or anxious -- remains underexplored. At the same time, large language models (LLMs) are increasingly used in health coaching, yet CGM is rarely incorporated as a first-class signal. To address this gap, we conducted a six-week autoethnography, combining CGM with multimodal indicators captured via common digital devices and a chatbot that offered personalized reflections and explanations of glucose fluctuations. Our findings show how CGM-led, data-first multimodal tracking, coupled with conversational support, shaped everyday practices of diet, activity, stress, and wellbeing. This work contributes to HCI by extending CGM research beyond clinical diabetes and demonstrating how LLM-driven agents can support preventive health and reflection in at-risk populations.

cs.HC↗

arXiv2025

Cross-Platform Short-Video Diplomacy: Topic and Sentiment Analysis of China-US Relations on Douyin and TikTok

We examine discussions surrounding China-U.S. relations on the Chinese and American social media platforms \textit{Douyin} and \textit{TikTok}. Both platforms, owned by \textit{ByteDance}, operate under different regulatory and cultural environments, providing a unique perspective for analyzing China-U.S. public discourse. This study analyzed 4,040 videos and 338,209 user comments to assess the public discussions and sentiments on social media regarding China-U.S. relations. Through topic clustering and sentiment analysis, we identified key themes, including economic strength, technological and industrial interdependence, cultural cognition and value pursuits, and responses to global challenges. There are significant emotional differences between China and the US on various themes. Since April 2022, the Chinese government has implemented a new regulation requiring all social media accounts to disclose their provincial-level geolocation information. Utilizing this publicly available data, along with factors such as GDP per capita, minority index, and internet penetration rate, we investigate the changes in sentiment towards the U.S. in mainland China. This study links socioeconomic indicators with online discussions, deeply analyzing how regional and economic factors influence Chinese comments on their views of the US, providing important insights for China-U.S. relationship research and policy making.

cs.SI↗

arXiv2025

Hollywood Town: Long-Video Generation via Cross-Modal Multi-Agent Orchestration

Recent advancements in multi-agent systems have demonstrated significant potential for enhancing creative task performance, such as long video generation. This study introduces three innovations to improve multi-agent collaboration. First, we propose OmniAgent, a hierarchical, graph-based multi-agent framework for long video generation that leverages a film-production-inspired architecture to enable modular specialization and scalable inter-agent collaboration. Second, inspired by context engineering, we propose hypergraph nodes that enable temporary group discussions among agents lacking sufficient context, reducing individual memory requirements while ensuring adequate contextual information. Third, we transition from directed acyclic graphs (DAGs) to directed cyclic graphs with limited retries, allowing agents to reflect and refine outputs iteratively, thereby improving earlier stages through feedback from subsequent nodes. These contributions lay the groundwork for developing more robust multi-agent systems in creative tasks.

cs.MA↗