1 citations · 1 across the 11 of their papers we have counts for
12 papers
ReDiS: Reasoning-Diagnosis Self-Evolution for Multimodal Reasoning in Remote Sensing
Yi Liu, Wentao Jiang, Shuo Ni +5
Multimodal large language models (MLLMs) have shown strong potential for remote sensing understanding, yet reliable reasoning remains challenging when tasks require fine-grained vi…
LocAnyMed: Vision-Language Grounding for Multimodal Medical Images
Zihan Wang, Tong Liu, Zhiwei Wang +6
Medical visual grounding connects free-form clinical queries to spatial evidence in medical images and is an important component of interpretable medical artificial intelligence. H…
Echo-DM: Ultrasound Marker Removal via Conditional Latent Diffusion and Region-Aware Fusion
Zhiwei Wang, Tao Huang, Wentao Jiang +7
Clinical ultrasound images often contain artificial markers, such as measurement calipers and text, to assist diagnostic interpretation and comparison. However, these markers can i…
SAMe: A Semantic Anatomy Mapping Engine for Robotic Ultrasound
Jing Zhang, Duojie Chen, Wentao Jiang +7
Robotic ultrasound has advanced local image-driven control, contact regulation, and view optimization, yet current systems lack the anatomical understanding needed to determine wha…
Echo-α: Large Agentic Multimodal Reasoning Model for Ultrasound Interpretation
Jing Zhang, Wentao Jiang, Tao Huang +8
Ultrasound interpretation requires both precise lesion localization and holistic clinical reasoning, yet existing methods typically excel at only one of these capabilities: special…
SARMAE: Masked Autoencoder for SAR Representation Learning
Danxu Liu, Di Wang, Hebaixu Wang +6
Synthetic Aperture Radar (SAR) imagery plays a critical role in all-weather, day-and-night remote sensing applications. However, existing SAR-oriented deep learning is constrained…
GeoZero: Incentivizing Reasoning from Scratch on Geospatial Scenes
Di Wang, Shunyu Liu, Wentao Jiang +10
Multimodal large language models (MLLMs) have undergone rapid development in advancing geospatial scene understanding. Recent studies have sought to enhance the reasoning capabilit…
REX-RAG: Reasoning Exploration with Policy Correction in Retrieval-Augmented Generation
Wentao Jiang, Xiang Feng, Zengmao Wang +5
Reinforcement learning (RL) is emerging as a powerful paradigm for enabling large language models (LLMs) to perform complex reasoning tasks. Recent advances indicate that integrati…
AnesSuite: A Comprehensive Benchmark and Dataset Suite for Anesthesiology Reasoning in LLMs
Xiang Feng, Wentao Jiang, Zengmao Wang +5
The application of large language models (LLMs) in the medical field has garnered significant attention, yet their reasoning capabilities in more specialized domains like anesthesi…
Benchmarking Reasoning Robustness in Large Language Models
Tong Yu, Yongcheng Jing, Xikun Zhang +6
Despite the recent success of large language models (LLMs) in reasoning such as DeepSeek, we for the first time identify a key dilemma in reasoning robustness and generalization: s…
Dynamic Parallel Tree Search for Efficient LLM Reasoning
Yifu Ding, Wentao Jiang, Shunyu Liu +9
Tree of Thoughts (ToT) enhances Large Language Model (LLM) reasoning by structuring problem-solving as a spanning tree. However, recent methods focus on search accuracy while overl…
LeMeViT: Efficient Vision Transformer with Learnable Meta Tokens for Remote Sensing Image Interpretation
Wentao Jiang, Jing Zhang, Di Wang +3
Due to spatial redundancy in remote sensing images, sparse tokens containing rich information are usually involved in self-attention (SA) to reduce the overall token numbers within…