3 citations · 6 across the 16 of their papers we have counts for
19 papers
CollabFlow: Recursive Self-Improvement of Agent Collaboration
Xiao Huang, Mingda Zhang, Junming Zhang +5
Recursive self-improvement (RSI) lets a system improve from its own outcomes; in LLM-based multi-agent systems, Agents refine one another within a task, and outcomes improve how th…
EvoSteer: Online Self-Evolving Graph Orchestration via Reference-Anchored Credit Assignment
Mingda Zhang, Hanwen Zhang, Qiang Huang +5
In recent years, LLM-based multi-agent systems have been widely applied to orchestrate tool-using agents into executable communication graphs. However, existing self-evolving orche…
SkillFlow: Flow-Driven Recursive Skill Evolution for Agentic Orchestration
Mingda Zhang, Tiesunlong Shen, Haoran Luo +4
In recent years, a variety of powerful LLM-based agentic systems have been applied to automate complex tasks through task orchestration. However, existing orchestration methods sti…
CS-MUNet: A Channel-Spatial Dual-Stream Mamba Network for Multi-Organ Segmentation
Yuyang Zheng, Mingda Zhang, Jianglong Qin +4
Recently Mamba-based methods have shown promise in abdominal organ segmentation. However, existing approaches neglect cross-channel anatomical semantic collaboration and lack expli…
FlowSteer: Towards Agents Designing Agentic Workflows via Reinforced Progressive Canvas Editing
Mingda Zhang, Wenjin Liu, Tiesunlong Shen +5
In recent years, agentic workflows have been widely applied to solve complex human tasks. However, existing workflow construction still faces key challenges, including human-depend…
Unified Multimodal Coherent Field: Synchronous Semantic-Spatial-Vision Fusion for Brain Tumor Segmentation
Mingda Zhang, Yuyang Zheng, Ruixiang Tang +2
Brain tumor segmentation requires accurate identification of hierarchical regions including whole tumor (WT), tumor core (TC), and enhancing tumor (ET) from multi-sequence magnetic…
A Semantic Segmentation Algorithm for Pleural Effusion Based on DBIF-AUNet
Ruixiang Tang, Mingda Zhang, Jianglong Qin +3
Pleural effusion semantic segmentation can significantly enhance the accuracy and timeliness of clinical diagnosis and treatment by precisely identifying disease severity and lesio…
A Multi-granularity Concept Sparse Activation and Hierarchical Knowledge Graph Fusion Framework for Rare Disease Diagnosis
Mingda Zhang, Na Zhao, Jianglong Qin +2
Despite advances from medical large language models in healthcare, rare-disease diagnosis remains hampered by insufficient knowledge-representation depth, limited concept understan…
An Integrated Framework of Prompt Engineering and Multidimensional Knowledge Graphs for Legal Dispute Analysis
Mingda Zhang, Na Zhao, Jianglong Qing +3
Legal dispute analysis is crucial for intelligent legal assistance systems. However, current LLMs face significant challenges in understanding complex legal concepts, maintaining r…
DCFFSNet: Deep Connectivity Feature Fusion Separation Network for Medical Image Segmentation
Mingda Zhang, Xun Ye, Ruixiang Tang +1
Medical image segmentation leverages topological connectivity theory to enhance edge precision and regional consistency. However, existing deep networks integrating connectivity of…
Multimodal Fusion at Three Tiers: Physics-Driven Data Generation and Vision-Language Guidance for Brain Tumor Segmentation
Mingda Zhang
Accurate brain tumor segmentation is crucial for neuro-oncology diagnosis and treatment planning. Deep learning methods have made significant progress, but automatic segmentation s…
Knowledge-Guided Brain Tumor Segmentation via Synchronized Visual-Semantic-Topological Prior Fusion
Mingda Zhang, Kaiwen Pan
Background: Brain tumor segmentation requires precise delineation of hierarchical structures from multi-sequence MRI. However, existing deep learning methods primarily rely on visu…
A Method for the Architecture of a Medical Vertical Large Language Model Based on Deepseek R1
Mingda Zhang, Jianglong Qin
Despite significant advances in foundation models like DeepSeek-R1 and ChatGPT, their deployment in medical settings faces critical challenges including computational requirements…
Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps
Nanye Ma, Shangyuan Tong, Haolin Jia +8
Generative models have made significant impacts across various domains, largely due to their ability to scale during training by increasing data, computational resources, and model…
OmnixR: Evaluating Omni-modality Language Models on Reasoning across Modalities
Lichang Chen, Hexiang Hu, Mingda Zhang +8
We introduce OmnixR, an evaluation suite designed to benchmark SoTA Omni-modality Language Models, such as GPT-4o and Gemini. Evaluating OLMs, which integrate multiple modalities s…
Video Summarization: Towards Entity-Aware Captions
Hammad A. Ayyubi, Tianqi Liu, Arsha Nagrani +7
Existing popular video captioning benchmarks and models deal with generic captions devoid of specific person, place or organization named entities. In contrast, news videos present…
Federated Learning of Shareable Bases for Personalization-Friendly Image Classification
Hong-You Chen, Jike Zhong, Mingda Zhang +5
Personalized federated learning (PFL) aims to harness the collective wisdom of clients' data while building personalized models tailored to individual clients' data distributions.…
BasisNet: Two-stage Model Synthesis for Efficient Inference
Mingda Zhang, Chun-Te Chu, Andrey Zhmoginov +6
In this work, we present BasisNet which combines recent advancements in efficient neural network architectures, conditional computation, and early termination in a simple new form.…
Domain-robust VQA with diverse datasets and methods but no target labels
Mingda Zhang, Tristan Maidment, Ahmad Diab +2
The observation that computer vision methods overfit to dataset specifics has inspired diverse attempts to make object recognition models robust to domain shifts. However, similar…