6 citations · 29 across the 47 of their papers we have counts for
21 papers · 1 filter
Fairness in Multi-modal Medical Diagnosis with Demonstration Selection
Dawei Li, Zijian Gu, Peng Wang +6
Multimodal large language models (MLLMs) have shown strong potential for medical image reasoning, yet fairness across demographic groups remains a major concern. Existing debiasing…
OR-R1: Automating Modeling and Solving of Operations Research Optimization Problem via Test-Time Reinforcement Learning
Zezhen Ding, Zhen Tan, Jiheng Zhang +1
Optimization modeling and solving are fundamental to the application of Operations Research (OR) in real-world decision making, yet the process of translating natural language prob…
Beyond Redundancy: Diverse and Specialized Multi-Expert Sparse Autoencoder
Zhen Xu, Zhen Tan, Song Wang +2
Sparse autoencoders (SAEs) have emerged as a powerful tool for interpreting large language models (LLMs) by decomposing token activations into combinations of human-understandable…
TRUST: A Decentralized Framework for Auditing Large Language Model Reasoning
Morris Yu-Chao Huang, Zhen Tan, Mohan Zhang +3
Large Language Models generate complex reasoning chains that reveal their decision-making, yet verifying the faithfulness and harmlessness of these intermediate steps remains a cri…
Leave It to the Experts: Detecting Knowledge Distillation via MoE Expert Signatures
Pingzhi Li, Morris Yu-Chao Huang, Zhen Tan +6
Knowledge Distillation (KD) accelerates training of large language models (LLMs) but poses intellectual property protection and LLM diversity risks. Existing KD detection methods b…
Can GRPO Help LLMs Transcend Their Pretraining Origin?
Kangqi Ni, Zhen Tan, Zijie Liu +2
Reinforcement Learning with Verifiable Rewards (RLVR), primarily driven by the Group Relative Policy Optimization (GRPO) algorithm, is a leading approach for enhancing the reasonin…
Multi-Agent Debate for LLM Judges with Adaptive Stability Detection
Tianyu Hu, Zhen Tan, Song Wang +2
With advancements in reasoning capabilities, Large Language Models (LLMs) are increasingly employed for automated judgment tasks. While LLMs-as-Judges offer promise in automating e…
Task-Aware Resolution Optimization for Visual Large Language Models
Weiqing Luo, Zhen Tan, Yifan Li +4
Real-world vision-language applications demand varying levels of perceptual granularity. However, most existing visual large language models (VLLMs), such as LLaVA, pre-assume a fi…
Metacognitive Self-Correction for Multi-Agent System via Prototype-Guided Next-Execution Reconstruction
Xu Shen, Qi Zhang, Song Wang +8
Large Language Model based multi-agent systems (MAS) excel at collaborative problem solving but remain brittle to cascading errors: a single faulty step can propagate across agents…
FaithCoT-Bench: Benchmarking Instance-Level Faithfulness of Chain-of-Thought Reasoning
Xu Shen, Song Wang, Zhen Tan +5
Large language models (LLMs) increasingly rely on Chain-of-Thought (CoT) prompting to improve problem-solving and provide seemingly transparent explanations. However, growing evide…
Quantum Variational Activation Functions Empower Kolmogorov-Arnold Networks
Jiun-Cheng Jiang, Morris Yu-Chao Huang, Tianlong Chen +1
Variational quantum circuits (VQCs) are central to quantum machine learning, while recent progress in Kolmogorov-Arnold networks (KANs) highlights the power of learnable activation…
Transferring Expert Cognitive Models to Social Robots via Agentic Concept Bottleneck Models
Xinyu Zhao, Zhen Tan, Maya Enisman +4
Successful group meetings, such as those implemented in group behavioral-change programs, work meetings, and other social contexts, must promote individual goal setting and executi…
EQA-RM: A Generative Embodied Reward Model with Test-time Scaling
Yuhang Chen, Zhen Tan, Tianlong Chen
Reward Models (RMs), vital for large model alignment, are underexplored for complex embodied tasks like Embodied Question Answering (EQA) where nuanced evaluation of agents' spatia…
Model Editing as a Double-Edged Sword: Steering Agent Ethical Behavior Toward Beneficence or Harm
Baixiang Huang, Zhen Tan, Haoran Wang +6
Agents based on Large Language Models (LLMs) have demonstrated strong capabilities across a wide range of tasks. However, deploying LLM-based agents in high-stakes domains comes wi…
IndustryEQA: Pushing the Frontiers of Embodied Question Answering in Industrial Scenarios
Yifan Li, Yuhang Chen, Anh Dao +5
Existing Embodied Question Answering (EQA) benchmarks primarily focus on household environments, often overlooking safety-critical aspects and reasoning processes pertinent to indu…
Are We Merely Justifying Results ex Post Facto? Quantifying Explanatory Inversion in Post-Hoc Model Explanations
Zhen Tan, Song Wang, Yifan Li +4
Post-hoc explanation methods provide interpretation by attributing predictions to input features. Natural explanations are expected to interpret how the inputs lead to the predicti…
Window Token Concatenation for Efficient Visual Large Language Models
Yifan Li, Wentao Bao, Botao Ye +4
To effectively reduce the visual tokens in Visual Large Language Models (VLLMs), we propose a novel approach called Window Token Concatenation (WiCo). Specifically, we employ a sli…
In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents
Zhen Tan, Jun Yan, I-Hung Hsu +12
Large Language Models (LLMs) have made significant progress in open-ended dialogue, yet their inability to retain and retrieve relevant information from long-term interactions limi…
GuideLLM: Exploring LLM-Guided Conversation with Applications in Autobiography Interviewing
Jinhao Duan, Xinyu Zhao, Zhuoxuan Zhang +12
Although Large Language Models (LLMs) succeed in human-guided conversations such as instruction following and question answering, the potential of LLM-guided conversations-where LL…
SCALE: Towards Collaborative Content Analysis in Social Science with Large Language Model Agents and Human Intervention
Chengshuai Zhao, Zhen Tan, Chau-Wai Wong +3
Content analysis breaks down complex and unstructured texts into theory-informed numerical categories. Particularly, in social science, this process usually relies on multiple roun…
BrainMAP: Learning Multiple Activation Pathways in Brain Networks
Song Wang, Zhenyu Lei, Zhen Tan +8
Functional Magnetic Resonance Image (fMRI) is commonly employed to study human brain activity, since it offers insight into the relationship between functional fluctuations and hum…