arXiv subjects

Qijia Chen

Publications and source records attributed to Qijia Chen.

5 recordsLinked to original sources

arXiv2026

Lexical Coupling in GUI Element Grounding: Sentence Embeddings Track Labels across Mobile and Web

GUI grounding evaluations that expose UI elements as text metadata often treat high instruction-element embedding similarity as evidence of semantic grounding. Across three mobile and web benchmarks, we show that this interpretation is frequently confounded by visible-label recovery. Lexical baselines remain competitive at top-1, label-poor targets remain weak for text-only methods, and encoder top-1 hits are predictable from lexical rank, candidate-pool size, and label type. We evaluate each action as a same-screen ranking task, comparing five off-the-shelf single-vector encoders with lexical baselines. Encoders recover some lexical misses, but deployable fusion gains are much smaller than target-aware oracle gains. These findings show that embedding-based evaluations can conflate visible-label recovery with semantic GUI grounding. Embedding-based evaluations should therefore report lexical baselines, label-type stratification, and deployable-fusion diagnostics. Our released repository provides analysis scripts and detexted per-step panels: https://github.com/qijia123/lexical-coupling-release.

cs.CL↗

arXiv2026

SkillDroid: Compile Once, Reuse Forever

LLM-based mobile GUI agents treat every task invocation as an independent reasoning episode, requiring a full LLM inference call at each action step. This per-step dependence makes them stateless: a task completed successfully yesterday is re-derived from scratch today, with no improvement in reliability or speed. We present SkillDroid, a three-layer skill agent that compiles successful LLM-guided GUI trajectories into parameterized skill templates (sequences of UI actions with weighted element locators and typed parameter slots) and replays them on future invocations without any LLM calls. A matching cascade (regex patterns, embedding similarity, and app filtering) routes incoming instructions to stored skills, while a failure-learning layer triggers recompilation when skill reliability degrades. Over a 150-round longitudinal evaluation with systematic instruction variation and controlled perturbations, SkillDroid achieves an 85.3% success rate (23 percentage points above a stateless LLM baseline) while using 49% fewer LLM calls. The skill replay mechanism achieves a perfect 1000% success rate across 79 replay rounds at 2.4 times the speed of full LLM execution. Most critically, the system improves with use: its success rate converges upward from 87% to 91%, while the baseline degrades from 80% to 44%.

cs.HC↗

arXiv2026

Understanding Newcomer Persistence in Social VR: A Case Study of VRChat

Newcomers are crucial for the growth of online communities, yet their successful integration into these spaces requires overcoming significant initial hurdles. Social Virtual Reality (VR) platforms are novel avenues that offer unprecedented online interaction experiences. Unlike well-studied two-dimensional online environments, the pathways to successful newcomer integration in online VR spaces are underexplored. Our research addresses this gap by examining the strategies used by newcomers to navigate early challenges in social VR and how they adapt. By focusing on active participants (ranging from newcomers currently navigating these hurdles to veterans who have successfully integrated) we isolate the specific strategies necessary for retention. We interviewed 24 active social VR users and conducted a reflexive thematic analysis. While participants identified barriers such as unfamiliar user interfaces, social norms, and overwhelming sensory input, our analysis reveals the adaptation strategies required to overcome them. Our findings expand on understanding newcomer persistence beyond traditional 2D environments, emphasizing how social dynamics influence the management of VR-specific issues like VR sickness during onboarding. Additionally, we highlight how successful newcomers overcome the lack of clear objectives in social VR by proactively constructing social meaning. We propose design suggestions to scaffold these successful integration pathways.

cs.HC↗

arXiv2026

Social, Spatial, and Self-Presence as Predictors of Basic Psychological Need Satisfaction in Social Virtual Reality

Extensive research has examined presence and basic psychological needs (drawing on Self-Determination Theory) in digital media. While prior work offers hints of potential connections, we lack a systematic account of whether and how distinct presence dimensions map onto the basic needs of autonomy, competence, and relatedness. We surveyed 301 social VR users and analyzed using Structural Equation Modeling. Results show that social presence predicts all three needs, while self-presence predicts competence and relatedness, and spatial presence shows no direct or moderating effects. Gender and age moderated these relationships: women benefited more from social presence for autonomy and relatedness, men from self- and spatial presence for competence and autonomy, and younger users showed stronger associations between social presence and relatedness, and between self-presence and autonomy. These findings position presence as a motivational mechanism shaped by demographic factors. The results offer theoretical insights and practical implications for designing inclusive, need-supportive multiuser VR environments.

cs.HC↗

arXiv2026

Usage Matters: The Role of Frequency, Duration, and Experience in Presence Formation in Social Virtual Reality

The sense of presence is central to immersive experiences in Virtual Reality (VR), and particularly salient in socially rich platforms like social VR. While prior studies have explored various aspects related to presence, less is known about how ongoing usage behaviors shape presence in everyday engagement. To address this gap, we examine whether usage intensity, captured through frequency of use, session duration, and years of VR experience, predicts presence in social VR. A survey of 295 users assessed overall, social, spatial, and self-presence using validated scales. Results show that both frequency and duration consistently predict higher presence across all dimensions, with interaction effects indicating that frequent and extended sessions synergistically amplify the experience of "being there." These effects were stable across age and gender. Our findings extend presence research beyond the laboratory by identifying behavioral predictors in social VR and offer insights for building inclusive environments that reliably foster presence.

cs.HC↗