4 citations · 9 across the 14 of their papers we have counts for
18 papers
VLMProbe: An Open-source Library for VLM Probing and Interpretation
Gexin Huang, Haisheng Fu, Yanting Yang +4
Understanding how vision-language models use visual information requires connecting internal representations to model behavior. We present vlmprobe, an open-source library that int…
EvalRx: Agentic Diagnosis and Verified Repair of Foundation Models
Jiaqi Liu, Ruinan Jin, Yitong Tang +4
Model evaluation reveals where models fail, but rarely why or whether a proposed fix genuinely works. We introduce EvalRx, an open-source agentic framework for statistically ground…
VisualCoding: Learning Executable Skills for Reliable Visual Reasoning
Jiaqi Liu, Liu Jiaqi, Ruinan Jin +3
Learning reusable skills can improve visual reasoning agents, yet applying a skill still requires the agent to implement or invoke a procedure and produce the final answer. Errors…
Self-Evolving Agents via Likelihood-Guided Tool-Space Optimization
Xuanqi Zhang, Ruinan Jin, Running Yang +4
Self-evolving agents can continually improve their behavior, while tools define the executable action space through which they interact with the environment. However, exposing the…
CPInj: Uncovering Prompt Injection Risks in Textual Collaborative Prompt Optimization
Xinting Liao, Behnoosh Zamanlooy, Masoumeh Shafieinejad +4
Textual Collaborative Prompt Optimization (TCPO) extends TextGrad (Yuksekgonul et al., 2025) to a decentralized setting by allowing multiple clients to jointly improve prompts for…
Precision Recall Controllable Radiology Report Generation via Hybrid Natural Language and Clinical Reward Learning
Ling Chen, Ruinan Jin, Jun Luo +6
Automated radiology report generation (RRG) has gained increasing attention because it can reduce the heavy workload of clinical report writing. However, most existing methods main…
Verification Mirage: Mapping the Reliability Boundary of Self-Verification in Medical VQA
Ruinan Jin, Beidi Zhao, Myeongkyun Kang +2
Self-verification, re-invoking the same vision language model (VLM) in a fresh context to check its own generated answer, is increasingly used as a default safety layer for medical…
DUCX: Decomposing Unfairness in Tool-Using Chest X-ray Agents
Zikang Xu, Ruinan Jin, Xiaoxiao Li
Fairness in medical agents is becoming critical as tool-using clinical AI systems orchestrate specialized vision and language modules for tasks such as chest X-ray question answeri…
RVCBench: Benchmarking the Robustness of Voice Cloning Across Modern Audio Generation Models
Ruinan Jin, Xinting Liao, Hanlin Yu +2
Modern Voice Cloning (VC) can synthesize speech that closely matches a target speaker from only seconds of reference audio, enabling applications such as personalized speech interf…
A Closer Look at Personalized Fine-Tuning in Heterogeneous Federated Learning
Minghui Chen, Hrad Ghoukasian, Ruinan Jin +3
Federated Learning (FL) enables decentralized, privacy-preserving model training but struggles to balance global generalization and local personalization due to non-identical data…
See-in-Pairs: Reference Image-Guided Comparative Vision-Language Models for Medical Diagnosis
Ruinan Jin, Gexin Huang, Xinwei Shen +3
Medical image diagnosis is challenging because many diseases resemble normal anatomy and exhibit substantial interpatient variability. Clinicians routinely rely on comparative diag…
Can Generalist Vision Language Models (VLMs) Rival Specialist Medical VLMs? Benchmarking and Strategic Insights
Yuan Zhong, Ruinan Jin, Qi Dou +1
Vision Language Models (VLMs) have shown promise in automating image diagnosis and interpretation in clinical settings. However, developing specialist medical VLMs requires substan…
Interactive Tumor Progression Modeling via Sketch-Based Image Editing
Gexin Huang, Ruinan Jin, Yucheng Tang +4
Accurately visualizing and editing tumor progression in medical imaging is crucial for diagnosis, treatment planning, and clinical communication. To address the challenges of subje…
Can Textual Gradient Work in Federated Learning?
Minghui Chen, Ruinan Jin, Wenlong Deng +4
Recent studies highlight the promise of LLM-based prompt optimization, especially with TextGrad, which automates differentiation'' via texts and backpropagates textual feedback. Th…
FairMedFM: Fairness Benchmarking for Medical Imaging Foundation Models
Ruinan Jin, Zikang Xu, Yuan Zhong +4
The advent of foundation models (FMs) in healthcare offers unprecedented opportunities to enhance medical diagnostics through automated classification and segmentation tasks. Howev…
Debiased Noise Editing on Foundation Models for Fair Medical Image Classification
Ruinan Jin, Wenlong Deng, Minghui Chen +1
In the era of Foundation Models' (FMs) rising prominence in AI, our study addresses the challenge of biases in medical images while the model operates in black-box (e.g., using FM…
Backdoor Attack on Unpaired Medical Image-Text Foundation Models: A Pilot Study on MedCLIP
Ruinan Jin, Chun-Yin Huang, Chenyu You +1
In recent years, foundation models (FMs) have solidified their role as cornerstone advancements in the deep learning domain. By extracting intricate patterns from vast datasets, th…
Forgettable Federated Linear Learning with Certified Data Unlearning
Ruinan Jin, Minghui Chen, Qiong Zhang +1
Federated Learning (FL) enables collaborative model training across distributed clients while preserving user privacy. Recently, Federated Unlearning (FU) has emerged to address th…