arXiv subjects

Shuowei Li

Publications and source records attributed to Shuowei Li.

6 recordsLinked to original sources

arXiv2026

CaRing: Preventing Carpal Tunnel Syndrome based on Daily Activities from Always-Available Input Device

We present CaRing, a ring worn on the base knuckle of the index finger, a wearable system for detecting the start and end of mouse use to help prevent Carpal Tunnel Syndrome, in which the damage to the median nerve is permanent. CaRing senses finger movement, which neither a software timer nor a wrist-worn device detects. The displacement reported by an optical flow sensor is accumulated into a running value, then a zero point is measured while the hand rests on the desk at the start of each session. With this formulation, the start and end thresholds are expressed relative to the session's zero point. CaRing does not introduce any per-user parameter. We empirically demonstrate that approximately $90\%$ of start and end events are detected within two seconds of the researcher's label, using 35 recordings and a lab study with ten users.

cs.HC↗

arXiv2026

FPGA Meets Breadboard: Integrating a Virtual Breadboard with Real FPGA Boards for Remote Access in Digital Design Courses

With the transition to remote instruction, new modalities for conducting hands-on labs are needed. In particular, courses that entail major hardware components faced challenges in making the hardware available for students in a reliable and sustainable way. This paper presents our experience in implementing a virtual breadboard feature to interface with real Field Programmable Gate Arrays (FPGAs) boards located on the University of Washington's campus, where students can access the boards remotely to complete their lab assignments.

cs.CY↗

arXiv2026

Effectiveness of Using Remote Laboratory in Promoting Simulation and Verification Tools

The transition to remote learning during the pandemic has necessitated the development of new methods for conducting hands-on experiments. One significant challenge in this transition has been providing students with reliable and sustainable access to necessary hardware components, particularly for courses that require substantial equipment. Additionally, industry partners have high expectations for students to be proficient in simulation and verification tools. To address these challenges, we implemented a virtual breadboard feature that allows students to remotely access Field Programmable Gate Array (FPGA) hardware and complete lab assignments. Our evaluation of this approach, which included surveys of students and industry partners, revealed that it effectively transformed a traditionally in-person lab assignment into an online modality. Furthermore, this paper presents the perspective of industry professionals on verification and simulation tools as a highly desirable skill in the industry, a skill that remote labs tend to emphasize which makes remote labs a viable educational solution that can continue to be utilized even after the pandemic.

cs.CY↗

arXiv2026

When Cultures Move: Measuring and Improving Multicultural Text-to-Video Generation

Text-to-video (T2V) generation has rapidly progressed in visual fidelity, yet its ability to faithfully represent multiple cultures within a single prompt remains underexplored. We introduce MAVEN, a multi-agent prompt refinement framework designed to improve cultural fidelity in both mono-cultural and cross-cultural T2V generation. MAVEN decomposes prompts into person, action, and location dimensions, handled by specialized agents operating in parallel or sequentially. To support systematic evaluation, we contribute a new benchmark of 243 culturally grounded prompts and 972 corresponding videos, spanning three cultures (Chinese, American, Romanian), three action categories, and both mono-cultural and cross-cultural scenarios. Evaluations combining CLIP-based metrics, VLM-as-judge assessments, and videoquality measures show that multi-agent refinement, particularly parallel specialization, significantly improves cultural relevance while preserving visual quality and temporal consistency. The dataset and code are available at https://github.com/AIM-SCU/MAVEN

cs.CV↗

arXiv2026

How Large Language Models Balance Internal Knowledge with User and Document Assertions

Large language models (LLMs) often need to balance their internal parametric knowledge with external information, such as user beliefs and content from retrieved documents, in real-world scenarios like RAG or chat-based systems. A model's ability to reliably process these sources is key to system safety. Previous studies on knowledge conflict and sycophancy are limited to a binary conflict paradigm, primarily exploring conflicts between parametric knowledge and either a document or a user, but ignoring the interactive environment where all three sources exist simultaneously. To fill this gap, we propose a three-source interaction framework and systematically evaluate 27 LLMs from 3 families on 2 datasets. Our findings reveal general patterns: most models rely more on document assertions than user assertions, and this preference is reinforced by post-training. Furthermore, our behavioral analysis shows that most models are impressionable, unable to effectively discriminate between helpful and harmful external information. To address this, we demonstrate that fine-tuning on diverse source interaction data can significantly increase a model's discrimination abilities. In short, our work paves the way for developing trustworthy LLMs that can effectively and reliably integrate multiple sources of information. Code is available at https://github.com/shuowl/llm-source-balancing.

cs.CL↗

arXiv2024

Does RAG Introduce Unfairness in LLMs? Evaluating Fairness in Retrieval-Augmented Generation Systems

Retrieval-Augmented Generation (RAG) has recently gained significant attention for its enhanced ability to integrate external knowledge sources into open-domain question answering (QA) tasks. However, it remains unclear how these models address fairness concerns, particularly with respect to sensitive attributes such as gender, geographic location, and other demographic factors. First, as language models evolve to prioritize utility, like improving exact match accuracy, fairness considerations may have been largely overlooked. Second, the complex, multi-component architecture of RAG methods poses challenges in identifying and mitigating biases, as each component is optimized for distinct objectives. In this paper, we aim to empirically evaluate fairness in several RAG methods. We propose a fairness evaluation framework tailored to RAG, using scenario-based questions and analyzing disparities across demographic attributes. Our experimental results indicate that, despite recent advances in utility-driven optimization, fairness issues persist in both the retrieval and generation stages. These findings underscore the need for targeted interventions to address fairness concerns throughout the RAG pipeline. The dataset and code used in this study are publicly available at this GitHub Repository https://github.com/elviswxy/RAG_fairness .

cs.CL↗