10 citations · 73 across the 42 of their papers we have counts for
44 papers
47B Mixture-of-Experts Beats 671B Dense Models on Chinese Medical Examinations
Chiung-Yi Tseng, Danyang Zhang, Tianyang Wang +8
The rapid advancement of large language models(LLMs) has prompted significant interest in their potential applications in medical domains. This paper presents a comprehensive bench…
Affective Multimodal Agents with Proactive Knowledge Grounding for Emotionally Aligned Marketing Dialogue
Lin Yu, Xiaofei Han, Yifei Kang +4
Recent advances in large language models (LLMs) have enabled fluent dialogue systems, but most remain reactive and struggle in emotionally rich, goal-oriented settings such as mark…
Is GPT-OSS All You Need? Benchmarking Large Language Models for Financial Intelligence and the Surprising Efficiency Paradox
Ziqian Bi, Danyang Zhang, Junhao Song +1
The rapid adoption of large language models in financial services necessitates rigorous evaluation frameworks to assess their performance, efficiency, and practical applicability.…
AutoSurvey2: Empowering Researchers with Next Level Automated Literature Surveys
Siyi Wu, Chiaxin Liang, Ziqian Bi +7
The rapid growth of research literature, particularly in large language models (LLMs), has made producing comprehensive and current survey papers increasingly difficult. This paper…
CoT-X: An Adaptive Framework for Cross-Model Chain-of-Thought Transfer and Optimization
Ziqian Bi, Yinzhi Wang, Tianyang Wang +6
Long Chain-of-Thought (CoT) traces can improve reasoning accuracy, but repeatedly generating them is costly for smaller or latency-constrained language models. This paper studies a…
Towards Alignment-Centric Paradigm: A Survey of Instruction Tuning in Large Language Models
Xudong Han, Junjie Yang, Tianyang Wang +4
Instruction tuning is a pivotal technique for aligning large language models (LLMs) with human intentions, safety constraints, and domain-specific requirements. This survey provide…
Is GPT-OSS Good? A Comprehensive Evaluation of OpenAI's Latest Open Source Models
Ziqian Bi, Keyu Chen, Chiung-Yi Tseng +9
In August 2025, OpenAI released GPT-OSS models, its first open weight large language models since GPT-2 in 2019, comprising two mixture of experts architectures with 120B and 20B p…
Exploring Efficiency Frontiers of Thinking Budget in Medical Reasoning: Scaling Laws between Computational Resources and Reasoning Quality
Ziqian Bi, Lu Chen, Junhao Song +15
This study presents the first comprehensive evaluation of thinking budget mechanisms in medical reasoning tasks, revealing fundamental scaling laws between computational resources…
Mixture of Experts in Large Language Models
Danyang Zhang, Junhao Song, Ziqian Bi +5
This paper presents a comprehensive review of the Mixture-of-Experts (MoE) architecture in large language models, highlighting its ability to significantly enhance model performanc…
Predicting ICU In-Hospital Mortality Using Adaptive Transformer Layer Fusion
Han Wang, Ruoyun He, Guoguang Lao +16
Early identification of high-risk ICU patients is crucial for directing limited medical resources. We introduce ALFIA (Adaptive Layer Fusion with Intelligent Attention), a modular,…
Multimodal Representation Learning and Fusion
Qihang Jin, Enze Ge, Yuhang Xie +8
Multi-modal learning is a fast growing area in artificial intelligence. It tries to help machines understand complex things by combining information from different sources, like im…
Achieving Trustworthy Real-Time Decision Support Systems with Low-Latency Interpretable AI Models
Zechun Deng, Ziwei Liu, Ziqian Bi +5
This paper investigates real-time decision support systems that leverage low-latency AI models, bringing together recent progress in holistic AI-driven decision tools, integration…
Feature Alignment and Representation Transfer in Knowledge Distillation for Large Language Models
Junjie Yang, Junhao Song, Xudong Han +9
Knowledge distillation (KD) is a technique for transferring knowledge from complex teacher models to simpler student models, significantly enhancing model efficiency and accuracy.…
Active Learning Methods for Efficient Data Utilization and Model Performance Enhancement
Chiung-Yi Tseng, Junhao Song, Ziqian Bi +4
In the era of data-driven intelligence, the paradox of data abundance and annotation scarcity has emerged as a critical bottleneck in the advancement of machine learning. This pape…
Advanced Deep Learning Methods for Protein Structure Prediction and Design
Yichao Zhang, Ningyuan Deng, Xinyuan Song +25
After AlphaFold won the Nobel Prize, protein prediction with deep learning once again became a hot topic. We comprehensively explore advanced deep learning methods applied to prote…
Generative Adversarial Networks Bridging Art and Machine Intelligence
Junhao Song, Yichao Zhang, Ziqian Bi +25
Generative Adversarial Networks (GAN) have greatly influenced the development of computer vision and artificial intelligence in the past decade and also connected art and machine i…
From In Silico to In Vitro: A Comprehensive Guide to Validating Bioinformatics Findings
Tianyang Wang, Silin Chen, Yunze Wang +18
Translating computational predictions into experimentally validated biological knowledge remains one of the central challenges in modern bioinformatics. This review critically exam…
From Aleatoric to Epistemic: Exploring Uncertainty Quantification Techniques in Artificial Intelligence
Tianyang Wang, Yunze Wang, Jun Zhou +16
Uncertainty quantification (UQ) is a critical aspect of artificial intelligence (AI) systems, particularly in high-risk domains such as healthcare, autonomous systems, and financia…
Deep Learning Model Security: Threats and Defenses
Tianyang Wang, Ziqian Bi, Yichao Zhang +24
Deep learning has transformed AI applications but faces critical security challenges, including adversarial attacks, data poisoning, model theft, and privacy leakage. This survey e…
From Noise to Nuance: Advances in Deep Generative Image Models
Benji Peng, Chia Xin Liang, Ziqian Bi +6
Deep learning-based image generation has undergone a paradigm shift since 2021, marked by fundamental architectural breakthroughs and computational innovations. Through reviewing a…
A Comprehensive Guide to Explainable AI: From Classical Models to LLMs
Weiche Hsieh, Ziqian Bi, Chuanqi Jiang +24
Explainable Artificial Intelligence (XAI) addresses the growing need for transparency and interpretability in AI systems, enabling trust and accountability in decision-making proce…
Deep Learning, Machine Learning, Advancing Big Data Analytics and Management
Weiche Hsieh, Ziqian Bi, Keyu Chen +23
Advancements in artificial intelligence, machine learning, and deep learning have catalyzed the transformation of big data analytics and management into pivotal domains for researc…
A Comprehensive Survey and Guide to Multimodal Large Language Models in Vision-Language Tasks
Chia Xin Liang, Pu Tian, Caitlyn Heqi Yin +7
This survey and application guide to multimodal large language models(MLLMs) explores the rapidly developing field of MLLMs, examining their architectures, applications, and impact…
From Pixels to Prose: Advancing Multi-Modal Language Models for Remote Sensing
Xintian Sun, Benji Peng, Charles Zhang +10
Remote sensing has evolved from simple image acquisition to complex systems capable of integrating and processing visual and textual data. This review examines the development and…