arXiv subjects

Yuwen Liu

Publications and source records attributed to Yuwen Liu.

6 recordsLinked to original sources

arXiv2026

"Pharos Night: Crown Pursuit": An AI-Native Deck-Building and Tactical Arena Game Design Based on Multi-Agent Systems

With advancements in generative AI technology, an increasing number of researchers have begun exploring AI-native games in which gameplay rules are directly driven by generative AI. This paper presents "Pharos Night: Crown Pursuit," an AI-native deck-building and tactical arena game based on a multi-agent system. The game uses large language models to generate materials and cards, support NPC decision-making, and mediate natural-language interactions. During play, players collect materials, describe desired card effects in natural language, and choose whether to negotiate or fight with NPCs in the arena. To constrain model-generated outcomes, the system parses responses as structured JSON, constructs card effects from predefined mechanics, and maps qualitative effect levels to designer-specified numerical values. A small-scale playtest with 13 participants suggests that the system can provide strategically meaningful and engaging AI-driven gameplay, while also revealing challenges related to predictability, transparency, and player control. This work demonstrates the potential of multi-agent generative AI systems for creating more emergent digital game experiences.

cs.HC↗

arXiv2026

Designing More Engaging Serious Games to Support Students' Mental Health: A Pilot Study Based on A CBT-Informed Design Framework

Addressing the issues of dullness, low compliance, and lack of appeal in current digital mental health education and serious games for students and adolescents, this study proposes a novel, experience-centered framework for serious game design: the Therapeutic Procedural Rhetoric and Mechanism Mapping Framework (TPR-MMF). Based on this framework, a side-scrolling serious game prototype, "World + You - World," was developed. This study compared the effectiveness of TPR-MMF-based games with traditional explicit educational serious games through a small-sample randomized controlled trial (N=28). The results of the Intrinsic Motivation Inventory (IMI) showed that the experimental group (playing "World + You - World") significantly outperformed the control group in four aspects. Furthermore, qualitative survey results indicated that players could perceive the psychological metaphors within the game mechanics and spontaneously resonated with real-life experiences. This study provides a highly engaging new development paradigm for gamified mental health education for students and adolescents.

cs.HC↗

arXiv2025

GENERator: A Long-Context Generative Genomic Foundation Model

The rapid advancement of DNA sequencing has produced vast genomic datasets, yet interpreting and engineering genomic function remain fundamental challenges. Recent large language models have opened new avenues for genomic analysis, but existing approaches are often limited by restricted training scope, constrained generative capability, or prohibitive computational cost. We introduce GENErator, a generative genomic foundation model for long-context DNA modeling, with a context length of 98k nucleotides, pre-trained on 386 billion nucleotides of eukaryotic DNA. Without task-specific fine-tuning, GENERator exhibits strong intrinsic capabilities: unsupervised embedding analyses reveal phylogenetically coherent structure, and sequence recovery benchmarks demonstrate generative accuracy comparable to or exceeding state-of-the-art models with substantially improved computational efficiency. In a zero-shot setting, GENERator achieves competitive variant effect prediction performance relative to alignment-based methods, while remaining fully alignment-free and broadly applicable across species. With task-specific fine-tuning, the model attains leading performance on established genomic benchmarks. We further demonstrate practical generative applications. GENERator can generate protein-coding DNA sequences that translate into structurally plausible proteins and, through a prompt-guided design framework, design cis-regulatory elements with targeted activity profiles, including synthetic super-enhancers validated by high-throughput UMI-STARR-seq assays. Together, these results establish GENERator as an efficient and biologically grounded framework for genomic interpretation and programmable sequence design. Code and supplementary resources are available at https://github.com/GenerTeam/GENERator.

cs.CL↗

arXiv2024

The Spike-and-Slab Quantile LASSO for Robust Variable Selection in Cancer Genomics Studies

Data irregularity in cancer genomics studies has been widely observed in the form of outliers and heavy-tailed distributions in the complex traits. In the past decade, robust variable selection methods have emerged as powerful alternatives to the non-robust ones to identify important genes associated with heterogeneous disease traits and build superior predictive models. In this study, to keep the remarkable features of the quantile LASSO and fully Bayesian regularized quantile regression while overcoming their disadvantage in the analysis of high-dimensional genomics data, we propose the spike-and-slab quantile LASSO through a fully Bayesian spike-and-slab formulation under the robust likelihood by adopting the asymmetric Laplace distribution (ALD). The proposed robust method has inherited the prominent properties of selective shrinkage and self-adaptivity to the sparsity pattern from the spike-and-slab LASSO (Ro\v{c}kov\'a and George, 2018). Furthermore, the spike-and-slab quantile LASSO has a computational advantage to locate the posterior modes via soft-thresholding rule guided Expectation-Maximization (EM) steps in the coordinate descent framework, a phenomenon rarely observed for robust regularization with non-differentiable loss functions. We have conducted comprehensive simulation studies with a variety of heavy-tailed errors in both homogeneous and heterogeneous model settings to demonstrate the superiority of the spike-and-slab quantile LASSO over its competing methods. The advantage of the proposed method has been further demonstrated in case studies of the lung adenocarcinomas (LUAD) and skin cutaneous melanoma (SKCM) data from The Cancer Genome Atlas (TCGA).

stat.ME↗

arXiv2024

A Survey on Recent Advances in LLM-Based Multi-turn Dialogue Systems

This survey provides a comprehensive review of research on multi-turn dialogue systems, with a particular focus on multi-turn dialogue systems based on large language models (LLMs). This paper aims to (a) give a summary of existing LLMs and approaches for adapting LLMs to downstream tasks; (b) elaborate recent advances in multi-turn dialogue systems, covering both LLM-based open-domain dialogue (ODD) and task-oriented dialogue (TOD) systems, along with datasets and evaluation metrics; (c) discuss some future emphasis and recent research problems arising from the development of LLMs and the increasing demands on multi-turn dialogue systems.

cs.CL↗

arXiv2021

Fast Wireless Sensor Anomaly Detection based on Data Stream in Edge Computing Enabled Smart Greenhouse

Edge computing enabled smart greenhouse is a representative application of Internet of Things technology, which can monitor the environmental information in real time and employ the information to contribute to intelligent decision-making. In the process, anomaly detection for wireless sensor data plays an important role. However, traditional anomaly detection algorithms originally designed for anomaly detection in static data have not properly considered the inherent characteristics of data stream produced by wireless sensor such as infiniteness, correlations and concept drift, which may pose a considerable challenge on anomaly detection based on data stream, and lead to low detection accuracy and efficiency. First, data stream usually generates quickly which means that it is infinite and enormous, so any traditional off-line anomaly detection algorithm that attempts to store the whole dataset or to scan the dataset multiple times for anomaly detection will run out of memory space. Second, there exist correlations among different data streams, which traditional algorithms hardly consider. Third, the underlying data generation process or data distribution may change over time. Thus, traditional anomaly detection algorithms with no model update will lose their effects. Considering these issues, a novel method (called DLSHiForest) on basis of Locality-Sensitive Hashing and time window technique in this paper is proposed to solve these problems while achieving accurate and efficient detection. Comprehensive experiments are executed using real-world agricultural greenhouse dataset to demonstrate the feasibility of our approach. Experimental results show that our proposal is practicable in addressing challenges of traditional anomaly detection while ensuring accuracy and efficiency.

cs.LG↗