collaborators

5 papers

cs.LG2026

Your Simulation Runs but Solves the Wrong Physics: PDE-Grounded Intent Verification for LLM-Generated Multiphysics Simulation Code

Zhenghan Song, Yulong Liu, Cheng Wan +4

Execution-based evaluation of LLM-generated code implicitly treats successful execution as a proxy for correctness. In scientific simulation, this proxy is insufficient: a generate…

cs.CL2026

MELD: Multi-Task Equilibrated Learning Detector for AI-Generated Text

Chenjun Li, Cheng Wan, Johannes C. Paetzold

Large language models are now embedded in everyday writing workflows, making reliable AI-generated text detection important for academic integrity, content moderation, and provenan…

cs.CV2026

Decoding the Pulse of Reasoning VLMs in Multi-Image Understanding Tasks

Chenjun Li

Multi-image reasoning remains a significant challenge for vision-language models (VLMs). We investigate a previously overlooked phenomenon: during chain-of-thought (CoT) generation…

cs.CV2025

Synthetic Vasculature and Pathology Enhance Vision-Language Model Reasoning

Chenjun Li, Cheng Wan, Laurin Lux +4

Vision-Language Models (VLMs) offer a promising path toward interpretable medical diagnosis by allowing users to ask about clinical explanations alongside predictions and across di…

cs.CV2025

Fine-tuning Vision Language Models with Graph-based Knowledge for Explainable Medical Image Analysis

Chenjun Li, Laurin Lux, Alexander H. Berger +3

Accurate staging of Diabetic Retinopathy (DR) is essential for guiding timely interventions and preventing vision loss. However, current staging models are hardly interpretable, an…