2026
MedVIGIL: Evaluating Trustworthy Medical VLMs Under Broken Visual Evidence
Hanqi Jiang,
Junhao Chen,
Yi Pan,
Lifeng Chen,
Weihang You,
Haozhen Gong,
Ruiyu Yan,
Jinglei Lv,
Lin Zhao,
Hui Ren,
Quanzheng Li,
Tianming Liu,
Xiang Li(† corresponding author)
NeurIPS 2026 (Evaluations & Datasets Track)
Paper
Project
Dataset
Blog
Abstract
MedVIGIL evaluates whether medical vision-language models recognize when visual evidence no longer supports an answer. The clinician-supervised benchmark tests false premises, wording changes, and image perturbations, measuring safe refusal and silent failure alongside answer correctness.
Genesis: Visual Augmentation Primitives for Multimodal Chain-of-Thought Reasoning
Fan Yang,
Lifeng Chen,
Yipu Wang,
Han Wang,
Feilong Chen,
Zhiyang Chen,
Yidong Ma,
Chaoyang Zhao, Hanqi Jiang,
Yibo Chen,
Xu Tang,
Yao Hu,
Ming Tang,
Xiaojie Jin,
Xiaolong Zheng,
Jinqiao Wang(† corresponding author)
EMNLP 2026
Who Speaks for the Pruned? Visual Token Pruning as Coverage Optimization
Qingchan Zhu,
Weihang You, Hanqi Jiang,
Changdi Yang,
Tianming Liu,
Geng Yuan(† corresponding author)
EMNLP 2026
Paper
Abstract
CoverPruner treats visual token pruning as a coverage optimization problem: each discarded token should be represented by a retained token. The training-free method combines coverage in projector space with query-dependent demand to preserve visual evidence while reducing inference cost.
SYNAPSE: Empowering LLM Agents with Episodic-Semantic Memory via Spreading Activation
Hanqi Jiang,
Junhao Chen,
Yi Pan,
Ling Chen,
Weihang You,
Yifan Zhou,
Ruidong Zhang,
Yohannes Abate,
Tianming Liu(† corresponding author)
Findings of ACL 2026
Paper
Code
Blog
Abstract
SYNAPSE organizes episodic and semantic memories in a unified graph. Spreading activation, lateral inhibition, and temporal decay identify relevant subgraphs, while hybrid retrieval combines graph activation with semantic similarity to support long-term agent memory.
ViThinker: Active Vision-Language Reasoning via Dynamic Perceptual Querying
Weihang You,
Qingchan Zhu,
David Liu,
Yi Pan,
Geng Yuan, Hanqi Jiang†(† corresponding author)
ICME 2026 Spotlight
Paper
Abstract
ViThinker enables vision-language models to generate perceptual queries during reasoning. A two-stage training curriculum distills frozen vision experts and learns task-driven querying, allowing the model to synthesize relevant visual features without external tool calls at inference time.
Quantum Artificial Intelligence: A Comprehensive Survey
Hanqi Jiang,
Yi Pan,
Junhao Chen,
Zhengliang Liu,
Lichao Sun,
Quanzheng Li,
Lu Zhang,
Dajiang Zhu,
Xianqiao Wang,
Wei Liu,
Xiang Li,
Gang Li,
Wei Zhang,
Lin Zhao,
Xiaowei Yu,
Yingfeng Wang,
Tianming Liu(† corresponding author)
Meta-Radiology 2026 IF=13.3
Paper
Abstract
This survey examines the bidirectional relationship between quantum computing and artificial intelligence, covering AI for quantum hardware and algorithms, quantum methods for learning, and the practical challenges of hybrid quantum-classical systems.
ADLGen: Synthesizing Symbolic, Event-Triggered Sensor Sequences for Human Activity Modeling
Weihang You*, Hanqi Jiang*,
Zishuai Liu,
Zihang Xie,
Tianming Liu,
Jin Lu,
Fei Dou†(* equal contribution)(† corresponding author)
SenSys 2026
Paper
Abstract
ADLGen synthesizes symbolic, event-triggered sensor sequences for human activity modeling, providing a novel approach to activity recognition and modeling.
2025
AdCare-VLM: Towards a Unified and Pre-aligned Latent Representation for Healthcare Video Understanding
Md Asaduzzaman Jabin, Hanqi Jiang,
Yiwei Li,
Patrick Kaggwa,
Eugene Douglass,
Juliet N. Sekandi,
Tianming Liu(† corresponding author)
NeurIPS Workshop on Holistic Video Understanding 2025 Oral
Bridging Classical and Quantum Computing for Next-Generation Language Models
Yi Pan*, Hanqi Jiang*,
Junhao Chen,
Yiwei Li,
Huaqin Zhao,
Lin Zhao,
Yohannes Abate,
Yingfeng Wang†,
Tianming Liu†(* equal contribution)(† corresponding author)
AAAI QIML 2025 Conference
Paper
Abstract
We introduce Adaptive Quantum-Classical Fusion (AQCF), the first framework to bridge quantum and classical computing through dynamic, quantum-classical co-design for next-generation language models.
Chatradio-valuer: A chat large language model for generalizable radiology report generation based on multi-institution and multi-system data
Tianyang Zhong,
Wei Zhao,
Yutong Zhang,
Yi Pan,
Peixin Dong,
Zuowei Jiang, Hanqi Jiang,
Yifan Zhou, et al.
(† corresponding author)
All 48 authors
Tianyang Zhong,
Wei Zhao,
Yutong Zhang,
Yi Pan,
Peixin Dong,
Zuowei Jiang, Hanqi Jiang,
Yifan Zhou,
Xiaoyan Kui,
Youlan Shang,
Lin Zhao,
Li Yang,
Yaonai Wei,
Zhuoyi Li,
Jiadong Zhang,
Longtao Yang,
Hao Chen,
Huan Zhao,
Yuxiao Liu,
Ning Zhu,
Yiwei Li,
Yisong Wang,
Jiaqi Yao,
Jiaqi Wang,
Ying Zeng,
Lei He,
Chao Zheng,
Zhixue Zhang,
Ming Li,
Zhengliang Liu,
Haixing Dai,
Zihao Wu,
Lu Zhang,
Shu Zhang,
Xiaoyan Cai,
Xintao Hu,
Shijie Zhao,
Xi Jiang,
Xin Zhang,
Wei Liu,
Xiang Li†,
Dajiang Zhu†,
Lei Guo†,
Dinggang Shen†,
Junwei Han†,
Tianming Liu†,
Jun Liu†,
Tuo Zhang†
IEEE Transactions on Biomedical Engineering 2025 Journal (IF=4.5)
Paper
Abstract
A chat large language model for generalizable radiology report generation based on multi-institution and multi-system data.
MolQAE: Quantum Autoencoder for Molecular Representation Learning
Yi Pan*, Hanqi Jiang*,
Wei Ruan,
Dajiang Zhu,
Xiang Li,
Yohannes Abate,
Yingfeng Wang†,
Tianming Liu†(* equal contribution)(† corresponding author)
QAI 2025 Conference
Paper
Abstract
Quantum Autoencoder for Molecular Representation Learning.
Argus: Leveraging Multi-View Images for Improved 3D Scene Understanding with Large Language Models
Yifan Xu,
Chao Zhang, Hanqi Jiang,
Xiaoyan Wang,
Ruifei Ma,
Yiwei Li,
Zihao Wu,
Zeju Li,
Xiangde Liu†(† corresponding author)
IEEE Transactions on Neural Networks and Learning Systems 2025 Journal (IF=10.2)
Paper
Abstract
Leveraging Multi-View Images for Improved 3D Scene Understanding with Large Language Models.
ECHOPulse: ECG Controlled Echocardiograms Video Generation
Yiwei Li,
Sekeun Kim,
Zihao Wu, Hanqi Jiang,
Yi Pan,
Pengfei Jin,
Sifan Song,
Yucheng Shi,
Xiaowei Yu,
Tianze Yang,
Tianming Liu†,
Quanzheng Li†,
Xiang Li†(† corresponding author)
ICLR 2025 Conference
Paper
Demo
Abstract
We propose ECHOPluse, an ECG-conditioned ECHO video generation model. ECHOPluse introduces two key advancements: (1) it accelerates ECHO video generation by leveraging VQ-VAE tokenization and masked visual token modeling for fast decoding, and (2) it conditions on readily accessible ECG signals, which are highly coherent with ECHO videos, bypassing complex conditional prompts. To the best of our knowledge, this is the first work to use time-series prompts like ECG signals for ECHO video generation. ECHOPluse not only enables controllable synthetic ECHO data generation but also provides updated cardiac function information for disease monitoring and prediction beyond ECG alone. Evaluations on three public and private datasets demonstrate state-of-the-art performance in ECHO video generation across both qualitative and quantitative measures.
EG-SpikeFormer: Eye-Gaze Guided Transformer on Spiking Neural Networks for Medical Image Analysis
Yi Pan*, Hanqi Jiang*,
Junhao Chen,
Yiwei Li,
Huaqin Zhao,
Yifan Zhou,
Peng Shu,
Zihao Wu,
Zhengliang Liu,
Dajiang Zhu,
Xiang Li,
Yohannes Abate,
Tianming Liu†(* equal contribution)(† corresponding author)
ISBI 2025 Oral Conference
Paper
Abstract
Eye-Gaze Guided Transformer on Spiking Neural Networks for Medical Image Analysis.
GeoDreamer: Mixed-Modality Graph for Geometry-Controllable 3D Indoor Scene Generation
Zhifei Yang,
Keyang Lu,
Chao Zhang,
Jiaxing Qi, Hanqi Jiang,
Ruifei Ma,
Shenglin Yin,
Yifan Xu,
Mingzhe Xing,
Zhen Xiao,
Jieyi Long,
Xiangde Liu†,
Guangyao Zhai†(† corresponding author)
AAAI 2025 Conference
Paper
Abstract
Mixed-Modality Graph for Geometry-Controllable 3D Indoor Scene Generation.
Artificial General Intelligence for Medical Imaging Analysis
Xiang Li,
Lin Zhao,
Lu Zhang,
Zihao Wu,
Zhengliang Liu, Hanqi Jiang,
Chao Cao,
Shaochen Xu, et al.
(† corresponding author)
All 19 authors
Xiang Li,
Lin Zhao,
Lu Zhang,
Zihao Wu,
Zhengliang Liu, Hanqi Jiang,
Chao Cao,
Shaochen Xu,
Yiwei Li,
Haixing Dai,
Yixuan Yuan,
Jun Liu,
Gang Li,
Dajiang Zhu,
Pingkun Yan,
Quanzheng Li,
Wei Liu,
Tianming Liu†,
Dinggang Shen†
IEEE Reviews in Biomedical Engineering 2025 Feature Article Journal (IF=17.2)
Paper
Abstract
A comprehensive review of artificial general intelligence for medical imaging analysis.
2024
Eye-gaze Guided Multi-modal Alignment for Medical Representation Learning
Chong Ma, Hanqi Jiang,
Wenting Chen,
Yiwei Li,
Zihao Wu,
Xiaowei Yu,
Zhengliang Liu,
Lei Guo,
Dajiang Zhu,
Tuo Zhang,
Dinggang Shen,
Tianming Liu†,
Xiang Li†(† corresponding author)
NeurIPS 2024 Conference
Paper
Abstract
We propose EGMA, a novel framework for medical multi-modal alignment, marking the first attempt to integrate eye-gaze data into vision-language pre-training. EGMA outperforms existing state-of-the-art medical multi-modal pre-training methods, and realizes notable enhancements in image classification and image-text retrieval tasks. EGMA demonstrates that even a small amount of eye-gaze data can effectively assist in multi-modal pre-training and improve the feature representation ability of the model.
Advancing Medical Radiograph Representation Learning: A Hybrid Pre-training Paradigm with Multilevel Semantic Granularity
Hanqi Jiang,
Xixuan Hao,
Yuzhou Huang,
Chong Ma,
Jiaxun Zhang,
Yi Pan,
Ruimao Zhang†(† corresponding author)
ECCV Workshop 2024 Conference
Paper
Abstract
We present a medical vision-language pre-training (Med-VLP) framework that incorporates multi-modal contrastive alignment and parallel generative streams with multi-level semantic hierarchies. To accomplish this goal, we effectively leverage the characteristics of medical data. By optimizing elaborate training objectives, our HybridMED is capable of efficiently executing a variety of downstream tasks, including cross-modal, uni-modal, and multi-modal types. Extensive experimental results demonstrate that our HybridMED can deliver highly satisfactory performance across a wide array of downstream tasks, thereby validating the model's superiority.
Depth-NeuS: Neural Implicit Surfaces Learning for Multi-view Reconstruction Based on Depth Information Optimization
Hanqi Jiang,
Cheng Zeng,
Runnan Chen,
Shuai Liang,
Yinhe Han†,
Yichao Gao,
Conglin Wang(† corresponding author)
ICIC 2024 Oral Conference
Paper
Abstract
Neural Implicit Surfaces Learning for Multi-view Reconstruction Based on Depth Information Optimization.
MFE-SSNet: Multi-Modal Fusion-Based End-to-End Steering Angle and Vehicle Speed Prediction Network
Yi Huang,
Wenzhuo Liu,
Yaoyu Li,
Lei Yang, Hanqi Jiang,
Zhiwei Li,
Jun Li†(† corresponding author)
Automotive Innovation 2024 Journal (IF=6.1)
Paper
Abstract
Multi-Modal Fusion-Based End-to-End Steering Angle and Vehicle Speed Prediction Network.