Research author
Rui Wang
13 AI research papers in the One9Founders library, with summaries and links to original sources.
Papers by Rui Wang
ExPhy: A Benchmark for Explicit Physical Property Learning in Multi-Object Trajectory Forecasting
Rui Wang, Yeteng Wu, Xianlin Zhang, et al.
Intern-S2-Mobius: Foundation Model with Decoupled Knowledge and Reasoning
Kai Chen, Jifeng Ding, Ning Ding, et al.
Intern-S2-Preview: Scientific Agentic Foundation Model
Lei Bai, Jiaqi Cao, Chiyu Chen, et al.
CodeRescue: Budget-Calibrated Recovery Routing for Coding Agents
Qijia He, Jiayi Cheng, Chenqian Le, et al.
OpenRCA 2.0: From Outcome Labels to Causal Process Supervision
Aoyang Fang, Yifan Yang, Jin'ao Shang, et al.
FreeStyle: Free Control of Style-Content Dual-Reference Generation from Community LoRA Mining
Jinghong Lan, Wei Cheng, Yunuo Chen, et al.
Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack
He Zhang, Lingzhu Xiang, Haitao Lin, et al.
ML-Embed: Inclusive and Efficient Embeddings for a Multilingual World
Ziyin Zhang, Zihan Liao, Hang Yu, et al.
A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations
Zihan Liu, Yizhen Wang, Rui Wang, et al.
TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale
Jun Wang, Ziyin Zhang, Rui Wang, et al.
Human-Centric Topic Modeling with Goal-Prompted Contrastive Learning and Optimal Transport
Rui Wang, Yi Zheng, Dongxin Wang, et al.
AVGen-Bench: A Task-Driven Benchmark for Multi-Granular Evaluation of Text-to-Audio-Video Generation
Ziwei Zhou, Zeyuan Lai, Rui Wang, et al.
F2LLM-v2: Inclusive, Performant, and Efficient Embeddings for a Multilingual World
Ziyin Zhang, Zihan Liao, Hang Yu, et al.
F2LLM-v2 is a new family of multilingual AI embedding models (ranging from small to large sizes) that can understand and work with over 200 languages, including many underserved languages that other models ignore. The researchers made these models more efficient and faster than previous versions while maintaining high performance, and they're releasing everything publicly to help other researchers build better AI systems.