Search Results for author: Haoran Guo

Found 6 papers, 3 papers with code

Adaptive Anomaly Recovery for Telemanipulation: A Diffusion Model Approach to Vision-Based Tracking

no code implementations11 Mar 2025 Haoyang Wang, Haoran Guo, Lingfeng Tao, Zhengxiong Li

Dexterous telemanipulation critically relies on the continuous and stable tracking of the human operator's commands to ensure robust operation.

continuous-control Continuous Control +1

CoSER: Coordinating LLM-Based Persona Simulation of Established Roles

1 code implementation13 Feb 2025 Xintao Wang, Heng Wang, Yifei Zhang, Xinfeng Yuan, Rui Xu, Jen-tse Huang, Siyu Yuan, Haoran Guo, Jiangjie Chen, Wei Wang, Yanghua Xiao, Shuchang Zhou

It provides authentic dialogues with real-world intricacies, as well as diverse data types such as conversation setups, character experiences and internal thoughts.

Think Thrice Before You Act: Progressive Thought Refinement in Large Language Models

no code implementations17 Oct 2024 Chengyu Du, Jinyi Han, Yizhou Ying, Aili Chen, Qianyu He, Haokun Zhao, Sirui Xia, Haoran Guo, Jiaqing Liang, Zulong Chen, Liangyue Li, Yanghua Xiao

To address these limitations, we propose Progressive Thought Refinement (PTR), a framework that enables LLMs to refine their responses progressively.

Avg

AgentGroupChat: An Interactive Group Chat Simulacra For Better Eliciting Emergent Behavior

1 code implementation20 Mar 2024 Zhouhong Gu, Xiaoxuan Zhu, Haoran Guo, Lin Zhang, Yin Cai, Hao Shen, Jiangjie Chen, Zheyu Ye, Yifei Dai, Yan Gao, Yao Hu, Hongwei Feng, Yanghua Xiao

Language significantly influences the formation and evolution of Human emergent behavior, which is crucial in understanding collective intelligence within human societies.

InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviews

2 code implementations27 Oct 2023 Xintao Wang, Yunze Xiao, Jen-tse Huang, Siyu Yuan, Rui Xu, Haoran Guo, Quan Tu, Yaying Fei, Ziang Leng, Wei Wang, Jiangjie Chen, Cheng Li, Yanghua Xiao

Then, with InCharacter, we show that state-of-the-art RPAs exhibit personalities highly aligned with the human-perceived personalities of the characters, achieving an accuracy up to 80. 7%.

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