arXiv subjects

Yuqing Wei

Publications and source records attributed to Yuqing Wei.

4 recordsLinked to original sources

arXiv2026

LLM-Based Invariant Testing for Software Functional Bugs

Manually writing unit tests to uncover functional bugs in software libraries is not only time-consuming but also requires a deep understanding of the intended semantics of the APIs. Heuristic-based test generation methods suffer from low usability because they cannot reason about program semantics or interpret source code and documentation as humans do. Traditional fuzzing techniques like OSS-Fuzz often rely on crashes to detect bugs, but functional bugs do not always cause crashes. To overcome these limitations, we present LISA, a novel LLM-based invariant testing framework for software functional bugs. LISA iteratively generates API sequences and program invariants guided by API n-gram feedback, achieving higher bug-detection rates and competitive code coverage compared with both fuzzing and prior LLM-based test generation approaches, and reporting each finding as a high-confidence bug candidate for developer confirmation.

cs.SE↗

arXiv2026

Reshaping Inclusive Interpersonal Dynamics through Smart Glasses in Mixed-Vision Social Activities

Meaningful social interaction is vital to well-being, yet Blind and Low Vision (BLV) individuals face persistent barriers when collaborating with sighted peers due to inaccessible visual cues. While most wearable assistive technologies emphasize individual tasks, smart glasses introduce opportunities for real-time, contextual support in social settings. To explore how smart glasses affect interpersonal dynamics and support inclusion in mixed-vision groups, we developed a smart glasses-based system, CollabLens, as a technology probe and employed it in four workshop sessions. We found that smart glasses can meaningfully support inclusive collaboration through expanding BLV participants' assistive networks with more flexible, independent access to visual information. While sighted participants viewed smart glasses as a promising medium that fosters interpersonal connection, they revealed uncertainty in adapting their helping behaviors. We concluded by discussing and synthesizing challenges and opportunities for designing smart glasses that provide seamless interaction experiences and enhance reciprocal mixed-vision social inclusion.

cs.HC↗

arXiv2026

ARIADNE: A Perception-Reasoning Synergy Framework for Trustworthy Coronary Angiography Analysis

Conventional pixel-wise loss functions fail to enforce topological constraints in coronary vessel segmentation, producing fragmented vascular trees despite high pixel-level accuracy. We present ARIADNE, a two-stage framework coupling preference-aligned perception with RL-based diagnostic reasoning for topologically coherent stenosis detection. The perception module employs DPO to fine-tune the Sa2VA vision-language foundation model using Betti number constraints as preference signals, aligning the policy toward geometrically complete vessel structures rather than pixel-wise overlap metrics. The reasoning module formulates stenosis localization as a Markov Decision Process with an explicit rejection mechanism that autonomously defers ambiguous anatomical candidates such as bifurcations and vessel crossings, shifting from coverage maximization to reliability optimization. On 1,400 clinical angiograms, ARIADNE achieves state-of-the-art centerline Dice of 0.838, reduces false positives by 41% compared to geometric baselines. External validation on multi-center benchmarks ARCADE and XCAD confirms generalization across acquisition protocols. This represents the first application of DPO for topological alignment in medical imaging, demonstrating that preference-based learning over structural constraints mitigates topological violations while maintaining diagnostic sensitivity in interventional cardiology workflows.

cs.CV↗

arXiv2025

Unlocking Mixed Reality for Medical Education: A See-Through Perspective on Head Anatomy

Extended reality (XR), encompassing Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), is emerging as a transformative platform for medical education. Traditional methods such as textbooks, physical models, and cadaveric dissections often lack interactivity and fail to convey complex spatial relationships effectively. The emerging MR technology addresses these limitations by providing immersive environments that blend virtual elements with real-world contexts. This study presents an MR application for head anatomy education, enabling learners to intuitively interact with see-through 3D anatomical structures via hand gestures and controllers. Our hierarchical information design supports progressive learning, guiding users from basic anatomical labels to detailed structural insights. Additionally, the system incorporates an automatic calibration module that aligns virtual anatomical models with a real human head, thereby facilitating realistic human-model interactions. Experiments show that the system can effectively match the anatomical model with real-time scenes, thus enhancing the interactivity and immersion of medical education, providing an innovative tool for teaching anatomy.

cs.HC↗