7 papers
EpiEvolve: Self-Evolving Agents for Streaming Pandemic Forecasting under Regime Shifts
Yiming Lu, Sihang Zeng, Zhengxu Tang +3
Epidemic LLM forecasters are usually trained and evaluated as static supervised models, whereas operational pandemic forecasting is a streaming process in which labels arrive after…
SeqBench: Benchmarking Sequential Narrative Generation in Text-to-Video Models
Zhengxu Tang, Zizheng Wang, Luning Wang +8
Text-to-video (T2V) generation models have made significant progress in creating visually appealing videos. However, they struggle with generating coherent sequential narratives th…
CCS: Controllable and Constrained Sampling with Diffusion Models via Initial Noise Perturbation
Bowen Song, Zecheng Zhang, Zhaoxu Luo +6
Diffusion models have emerged as powerful tools for generative tasks, producing high-quality outputs across diverse domains. However, how the generated data responds to the initial…
Multi-OphthaLingua: A Multilingual Benchmark for Assessing and Debiasing LLM Ophthalmological QA in LMICs
David Restrepo, Chenwei Wu, Zhengxu Tang +14
Current ophthalmology clinical workflows are plagued by over-referrals, long waits, and complex and heterogeneous medical records. Large language models (LLMs) present a promising…
Latent Space Disentanglement in Diffusion Transformers Enables Precise Zero-shot Semantic Editing
Zitao Shuai, Chenwei Wu, Zhengxu Tang +2
Diffusion Transformers (DiTs) have recently achieved remarkable success in text-guided image generation. In image editing, DiTs project text and image inputs to a joint latent spac…
Latent Space Disentanglement in Diffusion Transformers Enables Zero-shot Fine-grained Semantic Editing
Zitao Shuai, Chenwei Wu, Zhengxu Tang +2
Diffusion Transformers (DiTs) have achieved remarkable success in diverse and high-quality text-to-image(T2I) generation. However, how text and image latents individually and joint…