63 citations · 239 across the 76 of their papers we have counts for
80 papers
EEGBind: Detecting Source-Level Interictal Epileptiform Discharges via EEG-Centric Multimodal Binding
Muchen Li, Anglin Liu, Xuetian Gao +2
Source-level analysis of interictal epileptiform discharges (IEDs) is relevant to presurgical evaluation and treatment planning because it helps characterize where epileptiform act…
Coupled Graph--Policy Distillation for Personalized Medication Safety in Older Adults with Multimorbidity
Zihan Wang, Anglin Liu, Rongyi Wang +6
Large language model (LLM) agents can support medication review between clinical visits, but safe choices for older adults with multimorbidity depend on conditions, medications, an…
PATH: Next-Interval Prediction via Autoregressive Tree Hierarchy on Tabular Data
Pengxiang Cai, Wanchen Lian, Chenyang Liu +4
Interval prediction aims to achieve a target coverage level while producing intervals that are as short as possible. Many conformal regression pipelines first predict an uncertaint…
JustLLMGRPO: Radiographic Control for Chest X-Ray Generation
Pengxiang Cai, Xiaohan Li, Anglin Liu +3
Text-conditioned chest X-ray generation aims to synthesize realistic radiographs that faithfully depict specified findings. Existing work has primarily improved quality by updating…
Decoding Children's Gait Behavior
Yifan Shen, Boyi Li, Meihuan Huang +12
We introduce a new problem domain for human action recognition: the fine-grained analysis of children's gait behaviors from standard RGB video. We specifically target the ambulator…
Geometric Gradient Rectification for Safe Open-Set Semi-Supervised Learning
Jiahe Chen, Qian Shao, Qiyuan Chen +4
Open-set semi-supervised learning aims to leverage unlabeled data that may contain out-of-distribution outliers while maintaining performance on in-distribution classes. Existing m…
Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model
Pengxiang Cai, Tianchen Fang, Xiaohan Li +3
Reinforcement learning with verifiable rewards (RLVR) is widely viewed as a promising path toward continuously improving large language models. Recent works, however, suggest that…
APCyc: Property-Informed Design of Cyclic Peptides via Automated Cyclization
Yifan Zhao, Lang Qin, Jintai Chen
Cyclic peptides represent a promising class of therapeutic compounds in modern drug discovery, often offering improved stability and binding affinity. However, the de novo design o…
Can Broad Biomedical Knowledge be Contextualized into Scenario-Grounded Propositions?
Qingyuan Zeng, Ziyang Chen, Pengxiang Cai +5
Biomedical discovery often requires connecting broad biomedical knowledge with specific experimental or clinical data. Background knowledge suggests relevant mechanisms but is usua…
Don't Retrain, Just Reuse: Recovering Dual-Target Molecules from Single-Target Diffusion Models
Qingyuan Zeng, Pengxiang Cai, Zixin Guan +4
Designing a single molecule that modulates two targets is a promising strategy for polypharmacology, but it remains substantially harder than standard single-target generation beca…
Learning What Matters: Dynamic Dimension Selection and Aggregation for Interpretable Vision-Language Reward Modeling
Qiyuan Chen, Hongsen Huang, Jiahe Chen +6
Vision-language reward modeling faces a dilemma: generative approaches are interpretable but slow, while discriminative ones are efficient but act as opaque "black boxes." To bridg…
Med-Scout: Curing MLLMs' Geometric Blindness in Medical Perception via Geometry-Aware RL Post-Training
Anglin Liu, Ruichao Chen, Yi Lu +2
Despite recent Multimodal Large Language Models (MLLMs)' linguistic prowess in medical diagnosis, we find even state-of-the-art MLLMs suffer from a critical perceptual deficit: geo…
ClinicalReTrial: Clinical Trial Redesign with Self-Evolving Agents
Sixue Xing, Kerui Wu, Xuanye Xia +4
Clinical trials constitute a critical yet exceptionally challenging and costly stage of drug development ($2.6B per drug), where protocols are encoded as complex natural language…
Ophiuchus: Incentivizing Tool-augmented "Think with Images" for Joint Medical Segmentation, Understanding and Reasoning
Yankai Jiang, Yujie Zhang, Peng Zhang +5
Recent medical MLLMs have made significant progress in generating step-by-step textual reasoning chains. However, they still struggle with complex clinical tasks that necessitate d…
MedSAM3: Delving into Segment Anything with Medical Concepts
Anglin Liu, Rundong Xue, Xu R. Cao +5
Medical image segmentation is fundamental for biomedical discovery. Existing methods lack generalizability and demand extensive, time-consuming manual annotation for new clinical a…
Versatile and Risk-Sensitive Cardiac Diagnosis via Graph-Based ECG Signal Representation
Yue Wang, Yuyang Xu, Renjun Hu +7
Despite the rapid advancements of electrocardiogram (ECG) signal diagnosis and analysis methods through deep learning, two major hurdles still limit their clinical adoption: the la…
NEF-NET+: Adapting Electrocardio panorama in the wild
Zehui Zhan, Yaojun Hu, Jiajing Zhan +3
Conventional multi-lead electrocardiogram (ECG) systems capture cardiac signals from a fixed set of anatomical viewpoints defined by lead placement. However, certain cardiac condit…
PHASE: Physiology-Aware Hyperspectral Reconstruction via Object-to-Human Domain Adaptation
Yufei Wen, Shuxing Zhong, Jingdan Kang +3
Although hyperspectral imaging offers unparalleled non-invasive physiological insight, its bulky hardware, slow acquisition, and regulatory burden severely limit its clinical avail…
AstroMMBench: A Benchmark for Evaluating Multimodal Large Language Models Capabilities in Astronomy
Jinghang Shi, Xiaoyu Tang, Yang Huang +4
Astronomical image interpretation presents a significant challenge for applying multimodal large language models (MLLMs) to specialized scientific tasks. Existing benchmarks focus…
RareAgent: Self-Evolving Reasoning for Drug Repurposing in Rare Diseases
Lang Qin, Zijian Gan, Xu Cao +5
Computational drug repurposing for rare diseases is especially challenging when no prior associations exist between drugs and target diseases. Therefore, knowledge graph completion…
Strengthening LLMs for Tabular Prediction with Structural Priors
Pengxiang Cai, Zihao Gao, Wanchen Lian +2
Tabular prediction has long been dominated by gradient-boosted decision trees and specialized deep tabular models, while large language models (LLMs) remain difficult to make compe…
Icon: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation
Qiyuan Chen, Hongsen Huang, Qian Shao +6
Large Language Models (LLMs) require high quality preference datasets to align with human preferences. However, conventional methods for constructing such datasets face significant…
CC-GSEO-Bench: A Content-Centric Benchmark for Measuring Source Influence in Generative Search Engines
Qiyuan Chen, Jiahe Chen, Hongsen Huang +7
Generative Search Engines (GSEs) synthesize conversational answers from multiple sources, weakening the long-standing link between search ranking and digital visibility. This shift…
Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding
Chufan Gao, Jintai Chen, Jimeng Sun
Automated tabular understanding and reasoning are essential tasks for data scientists. Recently, Large language models (LLMs) have become increasingly prevalent in tabular reasonin…