3 citations · 3 across the 18 of their papers we have counts for
21 papers
PatientWM: Towards Generalizable Clinical World Models via Unified Patient States
Ziwen Kan, Jie Peng, Bo Yan +5
Clinical decisions depend on anticipating how a patient's condition will evolve as treatment is adjusted and new evidence becomes available. Existing medical world models capture p…
PrivMeSA: Privacy-Aware Self-Evolving Multi-Agent System for Medicine via Local-Remote LLM Collaboration
Dannong Wang, Yuran Zhang, Bian Sun +4
Clinical large language model (LLM) agents deployed locally can consult more capable remote models, but doing so risks exposing patient information. Privacy-conscious delegation pl…
Bridging Language and Physics: Automated Design of Continuum Robots with Large Language Models
Jingyi Chen, Mohan Zhang, Laura Yao +5
Large language models (LLMs) have recently emerged as a promising tool for automating robot design from high-level specifications, yet they remain ineffective for robots operating…
Dual-Cache Latent Space Communication between Heterogeneous Language Models
Jiyao Liu, Qi Zhang, Yaoyi Jia +2
Multi-agent LLM systems split work across models, so answering often requires knowledge that sits in another agent's context: a Sharer has encoded information that a Receiver needs…
SAF3R: Dynamic Sparse Attention for Feed-Forward 3D Reconstruction Transformers
Jianing Deng, Yuanzhe Li, Jialu Wang +4
Feed-forward 3D reconstruction (F3R) transformers have recently achieved remarkable success. However, scaling them to long image sequences remains challenging, as the quadratic com…
PAMF: Prior-Aware Multimodal Fusion for Incomplete Time Series Data
Ziwen Kan, Wugeng Zheng, Tianlong Chen +1
In healthcare, multimodal time series tasks often operate on incomplete observations in practice, for example when ECG segments are lost because electrodes detach or an entire resp…
TRACE: A Temporal Conditional Estimation for Multimodal Time Series Foundation Models
Ziwen Kan, Yishuo Chen, Kecheng Li +7
Time series foundation models (TS-FMs) aim to learn generalizable temporal representations that can be adapted to a wide range of downstream tasks. In real-world multimodal setting…
Detecting Unfaithful Chain-of-Thought via Circuit-Guided Internal-External Discrepancy
Xu Shen, Zhen Tan, Song Wang +4
Chain-of-thought (CoT) reasoning improves the problem-solving ability of large language models (LLMs), but generated reasoning traces may not faithfully reflect the model's actual…
GEMQ: Global Expert-Level Mixed-Precision Quantization for MoE LLMs
Jianing Deng, Song Wang, Dongwei Wang +4
Mixture-of-Experts Large Language Models (MoE-LLMs) achieve strong performance but incur substantial memory overhead due to massive expert parameters. Mixed-precision quantization…
MuteBench: Modality Unavailability Tolerance Evaluation for Incomplete Multimodal Fusion
Wugeng Zheng, Ziwen Kan, Tianlong Chen +3
Multimodal physiological data powers clinical AI systems from intensive care units to wearable devices, but sensors routinely fail in practice. Two failure modes are common: modali…
Probing to Refine: Reinforcement Distillation of LLMs via Explanatory Inversion
Zhen Tan, Chengshuai Zhao, Song Wang +3
Distilling robust reasoning capabilities from large language models (LLMs) into smaller, computationally efficient student models remains an unresolved challenge. Despite recent ad…
Interpretable Alzheimer's Diagnosis via Multimodal Fusion of Regional Brain Experts
Farica Zhuang, Shu Yang, Dinara Aliyeva +6
Accurate and early diagnosis of Alzheimer's disease (AD) is critical for effective intervention and requires integrating complementary information from multimodal neuroimaging data…
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…
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…
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…
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…
FIER: Fine-Grained and Efficient KV Cache Retrieval for Long-context LLM Inference
Dongwei Wang, Zijie Liu, Song Wang +5
The Key-Value (KV) cache reading latency increases significantly with context lengths, hindering the efficiency of long-context LLM inference. To address this, previous works propo…
The Quest for Efficient Reasoning: A Data-Centric Benchmark to CoT Distillation
Ruichen Zhang, Rana Muhammad Shahroz Khan, Zhen Tan +3
Data-centric distillation, including data augmentation, selection, and mixing, offers a promising path to creating smaller, more efficient student Large Language Models (LLMs) that…
Efficient MAP Estimation of LLM Judgment Performance with Prior Transfer
Huaizhi Qu, Inyoung Choi, Zhen Tan +6
LLM ensembles are widely used for LLM judges. However, how to estimate their accuracy, especially in an efficient way, is unknown. In this paper, we present a principled maximum a…
Tuning-Free Accountable Intervention for LLM Deployment -- A Metacognitive Approach
Zhen Tan, Jie Peng, Tianlong Chen +1
Large Language Models (LLMs) have catalyzed transformative advances across a spectrum of natural language processing tasks through few-shot or zero-shot prompting, bypassing the ne…