15 papers
Predicting Immune Biomarkers with MultiModal Mixture-of-Expert Pathology Foundation Models Empowers Precision Oncology
Tianyu Liu, Ziqing Wang, Zhaokang Liang +12
Predicting immune biomarkers associated with the tumor immune microenvironment (TIME) is critical for advancing precision oncology, yet existing approaches are largely limited to s…
Can Post-Training Turn LLMs into Good Medical Coders? An Empirical Study of Generative ICD Coding
Ziqing Wang, Weihao Li, Shijie Chen +2
Automated International Classification of Diseases (ICD) coding is a core medical-coding task for billing, epidemiology, and clinical decision support. Generative large language mo…
Benchmarking AI Agents for Addressing Scientific Challenges Across Scales
Tianyu Liu, Allen Xin Wang, Antonia Panescu +30
AI agents are increasingly being developed to accelerate scientific discovery, yet their practical capabilities in real research settings remain poorly understood. Existing benchma…
LatentDx: Latent Multi-Agent Communication for Cross-Hospital Rare-Disease Diagnosis
Ziqing Wang, Lili Zhao, Kaize Ding
Rare diseases affect over million patients across more than conditions, yet no single hospital encounters enough cases of any one condition for reliable diagnosis.…
MedLoCoMo: A Long-Context Multi-Session Medical Dialogue Benchmark for Large Language Models
Zeyu Zhang, Ziqing Wang, Kaize Ding
MedLoCoMo is a Medical Long-Context Memory benchmark for patient-specific clinical reasoning over multi-admission medical dialogue. Existing medical QA benchmarks largely test shor…
HopRank: Self-Supervised LLM Preference-Tuning on Graphs for Few-Shot Node Classification
Ziqing Wang, Kaize Ding
Node classification on text-attributed graphs (TAGs) is a fundamental task with broad applications in citation analysis, social networks, and recommendation systems. Current GNN-ba…
WavAlign: Enhancing Intelligence and Expressiveness in Spoken Dialogue Models via Adaptive Hybrid Post-Training
Yifu Chen, Shengpeng Ji, Qian Chen +9
End-to-end spoken dialogue models have garnered significant attention because they offer a higher potential ceiling in expressiveness and perceptual ability than cascaded systems.…
Dual-Axis Generative Reward Model Toward Semantic and Turn-taking Robustness in Interactive Spoken Dialogue Models
Yifu Chen, Shengpeng Ji, Zhengqing Liu +6
Achieving seamless, human-like interaction remains a key challenge for full-duplex spoken dialogue models (SDMs). Reinforcement learning (RL) has substantially enhanced text- and v…
MolMem: Memory-Augmented Agentic Reinforcement Learning for Sample-Efficient Molecular Optimization
Ziqing Wang, Yibo Wen, Abhishek Pandy +2
In drug discovery, molecular optimization aims to iteratively refine a lead compound to improve molecular properties while preserving structural similarity to the original molecule…
GlassMol: Interpretable Molecular Property Prediction with Concept Bottleneck Models
Oscar Rivera, Ziqing Wang, Matthieu Dagommer +2
Machine learning accelerates molecular property prediction, yet state-of-the-art Large Language Models and Graph Neural Networks operate as black boxes. In drug discovery, where sa…
RAPTOR: Ridge-Adaptive Logistic Probes
Ziqi Gao, Yaotian Zhu, Qingcheng Zeng +4
Probing studies what information is encoded in a frozen LLM's layer representations by training a lightweight predictor on top of them. Beyond analysis, probes are often used opera…
AMANDA: Agentic Medical Knowledge Augmentation for Data-Efficient Medical Visual Question Answering
Ziqing Wang, Chengsheng Mao, Xiaole Wen +2
Medical Multimodal Large Language Models (Med-MLLMs) have shown great promise in medical visual question answering (Med-VQA). However, when deployed in low-resource settings where…
POLO: Preference-Guided Multi-Turn Reinforcement Learning for Lead Optimization
Ziqing Wang, Yibo Wen, William Pattie +6
Lead optimization in drug discovery requires efficiently navigating vast chemical space through iterative cycles to enhance molecular properties while preserving structural similar…
A Survey of Large Language Models for Text-Guided Molecular Discovery: from Molecule Generation to Optimization
Ziqing Wang, Kexin Zhang, Zihan Zhao +4
Large language models (LLMs) are introducing a paradigm shift in molecular discovery by enabling text-guided interaction with chemical spaces through natural language, symbolic not…
WavRAG: Audio-Integrated Retrieval Augmented Generation for Spoken Dialogue Models
Yifu Chen, Shengpeng Ji, Haoxiao Wang +5
Retrieval Augmented Generation (RAG) has gained widespread adoption owing to its capacity to empower large language models (LLMs) to integrate external knowledge. However, existing…