| 2026 | ACL | Do We Always Need Query-Level Workflows? Rethinking Agentic Workflow Generation for Multi-Agent Systems. | Zixu Wang, Bingbing Xu, Yige Yuan, Huawei Shen, Xueqi Cheng |
| 2026 | EACL | Incentivizing Strong Reasoning from Weak Supervision. | Yige Yuan, Teng Xiao, Shuchang Tao, Xue Wang, Jinyang Gao, Bolin Ding, Bingbing Xu |
| 2026 | WSDM | Multi-Personality Generation of LLMs at Decoding-time. | Rongxin Chen, Yunfan Li, Yige Yuan, Bingbing Xu, Huawei Shen |
| 2025 | ACL | From Outcomes to Processes: Guiding PRM Learning from ORM for Inference-Time Alignment. | Bin Xie, Bingbing Xu, Yige Yuan, Shengmao Zhu, Huawei Shen |
| 2025 | CIKM | The 1st Workshop on LLM Agents for Social Simulation. | Yige Yuan, Junkai Zhou, Bingbing Xu, Liang Pang, Du Su, An Zhang, Teng Xiao, Fengli Xu, Zhaochun Ren, Xu Chen |
| 2025 | ICLR | SimPER: A Minimalist Approach to Preference Alignment without Hyperparameters. | Teng Xiao, Yige Yuan, Zhengyu Chen, Mingxiao Li, Shangsong Liang, Zhaochun Ren, Vasant G. Honavar |
| 2025 | ICLR | On a Connection Between Imitation Learning and RLHF. | Teng Xiao, Yige Yuan, Mingxiao Li, Zhengyu Chen, Vasant G. Honavar |
| 2025 | ICPADS | Adaptive Multiscale Decomposition Echo State Network for Chaotic Time Series Prediction. | Jing Zhang, XiaoDan He, Yige Yuan, Yang Yang |
| 2025 | WWW | Unveiling the Potential of LLMs in Simulated Society: A Knowledge-Driven LLM Agent Framework for User Modeling. | Shengmao Zhu, Bingbing Xu, Yige Yuan, Bin Xie, Yunfan Li, Huawei Shen |
| 2025 | SIGIR | InfoNCE is a Free Lunch for Semantically guided Graph Contrastive Learning. | Zixu Wang, Bingbing Xu, Yige Yuan, Huawei Shen, Xueqi Cheng |
| 2025 | SIGIR | Fact-Level Calibration and Correction for Long-Form Generations. | Yige Yuan, Bingbing Xu, Hexiang Tan, Fei Sun, Teng Xiao, Wei Li, Huawei Shen, Xueqi Cheng |
| 2024 | AAAI | PDE+: Enhancing Generalization via PDE with Adaptive Distributional Diffusion. | Yige Yuan, Bingbing Xu, Bo Lin, Liang Hou, Fei Sun, Huawei Shen, Xueqi Cheng |
| 2024 | CVPR | TEA: Test-Time Energy Adaptation. | Yige Yuan, Bingbing Xu, Liang Hou, Fei Sun, Huawei Shen, Xueqi Cheng |
| 2024 | DASFAA | History Driven Sampling for Scalable Graph Neural Networks. | Yang Li, Bingbing Xu, Fei Sun, Qi Cao, Yige Yuan, Huawei Shen, Xueqi Cheng |
| 2024 | EMNLP | How to Leverage Demonstration Data in Alignment for Large Language Model? A Self-Imitation Learning Perspective. | Teng Xiao, Mingxiao Li, Yige Yuan, Huaisheng Zhu, Chao Cui, Vasant G. Honavar |
| 2024 | SIGIR | Negative as Positive: Enhancing Out-of-distribution Generalization for Graph Contrastive Learning. | Zixu Wang, Bingbing Xu, Yige Yuan, Huawei Shen, Xueqi Cheng |