Research author

Yifan Yang

12 AI research papers in the One9Founders library, with summaries and links to original sources.

Papers by Yifan Yang

Sep 10, 2026
cs.LG

CoRA-NAS: Coarse Ranking and Anchor-Residual Refinement for Neural Architecture Search

Yifan Yang, Zhaoyan Wang, Zheng Gao, et al.

Aug 25, 2026
cs.AI

RACE: Scalable Statistical Estimation of Functional Consistency in LLM Neurons

Runyu Wang, Bo Liu, Xiaxin Zhang, et al.

Jul 16, 2026
cs.CL

SciDiagramEdit: Learning to Edit Scientific Diagrams from Paper Revisions

Yasheng Sun, Zezi Zeng, Yifan Yang, et al.

Jul 9, 2026
cs.AI

Ideas Have Genomes: Benchmarking Scientific Lineage Reasoning and Lineage-Grounded Idea Generation

Yifan Zhou, Qihao Yang, Yan Li, et al.

32
Jun 25, 2026
cs.AI

OpenRCA 2.0: From Outcome Labels to Causal Process Supervision

Aoyang Fang, Yifan Yang, Jin'ao Shang, et al.

Jun 5, 2026
cs.SD

MMAE: A Massive Multitask Audio Editing Benchmark

Ziyang Ma, Ruiqi Yan, Ruiyang Xu, et al.

12
May 14, 2026
cs.CV

VGGT-Edit: Feed-forward Native 3D Scene Editing with Residual Field Prediction

Kaixin Zhu, Yiwen Tang, Yifan Yang, et al.

23
May 5, 2026
cs.AI

SOAR: Real-Time Joint Optimization of Order Allocation and Robot Scheduling in Robotic Mobile Fulfillment Systems

Yibang Tang, Yifan Yang, Jingyuan Wang, et al.

Apr 28, 2026
cs.CV

Toward Multimodal Conversational AI for Age-Related Macular Degeneration

Ran Gu, Benjamin Hou, Mélanie Hébert, et al.

Apr 16, 2026
cs.CV

MM-WebAgent: A Hierarchical Multimodal Web Agent for Webpage Generation

Yan Li, Zezi Zeng, Yifan Yang, et al.

6
Apr 9, 2026
cs.CV

AVGen-Bench: A Task-Driven Benchmark for Multi-Granular Evaluation of Text-to-Audio-Video Generation

Ziwei Zhou, Zeyuan Lai, Rui Wang, et al.

2
Mar 19, 2026
cs.CV

Em-Garde: A Propose-Match Framework for Proactive Streaming Video Understanding

Yikai Zheng, Xin Ding, Yifan Yang, et al.

Em-Garde is a new system for understanding video streams in real-time that can proactively answer user questions about what's happening in videos. Instead of checking every frame to decide when to respond (which is slow and inaccurate), it converts user questions into visual search patterns and efficiently matches them against the incoming video stream, achieving better accuracy with less computational effort.

video understandingstreaming videoVideoLLMreal-time processing
Yifan Yang — AI Research Papers | One9Founders