activity
20242026
most citedFlow: Modularized Agentic Workflow Automation

1 citations · 1 across the 6 of their papers we have counts for

collaborators

7 papers

cs.LG2026

SubFlow: Sub-mode Conditioned Flow Matching for Diverse One-Step Generation

Yexiong Lin, Jia Shi, Shanshan Ye +3

Flow matching has emerged as a powerful generative framework, with recent few-step methods achieving remarkable inference acceleration. However, we identify a critical yet overlook…

cs.CV2025

DINO-Detect: A Simple yet Effective Framework for Blur-Robust AI-Generated Image Detection

Jialiang Shen, Jiyang Zheng, Yunqi Xue +8

With growing concerns over image authenticity and digital safety, the field of AI-generated image (AIGI) detection has progressed rapidly. Yet, most AIGI detectors still struggle u…

cs.AI2025

MedDCR: Learning to Design Agentic Workflows for Medical Coding

Jiyang Zheng, Islam Nassar, Thanh Vu +5

Medical coding converts free-text clinical notes into standardized diagnostic and procedural codes, which are essential for billing, hospital operations, and medical research. Unli…

cs.AI2025

A Sample Efficient Conditional Independence Test in the Presence of Discretization

Boyang Sun, Yu Yao, Xinshuai Dong +4

In many real-world scenarios, interested variables are often represented as discretized values due to measurement limitations. Applying Conditional Independence (CI) tests directly…

cs.CV2025

Beyond Optimal Transport: Model-Aligned Coupling for Flow Matching

Yexiong Lin, Yu Yao, Tongliang Liu

Flow Matching (FM) is an effective framework for training a model to learn a vector field that transports samples from a source distribution to a target distribution. To train the…

cs.AI2025★ 1 cited

Flow: Modularized Agentic Workflow Automation

Boye Niu, Yiliao Song, Kai Lian +4

Multi-agent frameworks powered by large language models (LLMs) have demonstrated great success in automated planning and task execution. However, the effective adjustment of agenti…

cs.CV2024

Ranked from Within: Ranking Large Multimodal Models Without Labels

Weijie Tu, Weijian Deng, Dylan Campbell +4

Can the relative performance of a pre-trained large multimodal model (LMM) be predicted without access to labels? As LMMs proliferate, it becomes increasingly important to develop…