Zeyuan Yang

I am a first year PhD student at University of Massachusetts Amherst, working under the supervision of Prof. Chuang Gan and Prof. Hao Zhang.

I received my master's degree in Computer Science at Tsinghua University. During my master study, I was fortunate to be mentored by Prof. Yang Liu and Prof. Peng Li. Before graduate study, I received my bachelor's degree from the School of Economics and Management at Tsinghua University.

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Research

I am broadly interested in embodied intelligence and multi-modal foundation models. Currently, I focus on building lifelong embodied agents executable in real-world environments. I welcome collaboration opportunities and encourages interested individuals to reach out.

clean-usnob VCA: Video Curious Agent for Long Video Understanding
Zeyuan Yang, Delin Chen, Xueyang Yu, Maohao Shen, Chuang Gan
arXiv, 2024
Project | Paper

In this work, we introduce VCA, a curiosity-driven video agent with self-exploration capability, which autonomously navigates video segments and efficiently builds a comprehensive understanding of complex video sequences.

clean-usnob Rethinking Long Context Generation from the Continual Learning Perspective
Zeyuan Yang, Fangzhou Xiong, Peng Li, Yang Liu
COLING, 2025

In this paper, we inspect existing representative approaches and analyze their synergy with continual learning strategies. Also, we integrate these strategies into current approaches to further boost LLMs' efficiency in processing long contexts.

UBSoft: A Simulation Platform for Robotic Skill Learning in Unbounded Soft Environments
Chunru Lin, Jungang Fan, Yian Wang, Zeyuan Yang, Zhehuan Chen, Lixin Fang, Tsun-Hsuan Wang, Xian Zhou, Chuang Gan
CoRL, 2024
Project | Paper | Code

In this paper, we introduce UBSOFT, a new simulation platform designed to support unbounded soft environments for robot skill acquisition.

clean-usnob Towards Unified Alignment Between Agents, Humans, and Environment
Zonghan Yang, An Liu, Zijun Liu, Kaiming Liu, Fangzhou Xiong, Yile Wang, Zeyuan Yang, Qingyuan Hu, Xinrui Chen, Zhenhe Zhang, Fuwen Luo, Zhicheng Guo, Peng Li, Yang Liu
ICML, 2024
Project | Paper | Code

In this work, we introduce the principles of Unified Alignment for Agents (UA2), which advocate for the simultaneous alignment of agents with human intentions, environmental dynamics, and self-constraints such as the limitation of monetary budgets.

clean-usnob RILA: Reflective and Imaginative Language Agent for Zero-Shot Semantic Audio-Visual Navigation
Zeyuan Yang, Jiageng Liu, Peihao Chen, Anoop Cherian, Tim K. Marks, Jonathan Le Roux, Chuang Gan
CVPR, 2024
Paper

In this work, we propose RILA, a reflective and imaginative agent for zero-shot semantic audio-visual navigation.

clean-usnob Failures Pave the Way: Enhancing Large Language Models through Tuning-free Rule Accumulation
Zeyuan Yang, Peng Li, Yang Liu
EMNLP, 2023
Paper | Code

In this work, we propose our Tuning-free Rule Accumulation (TRAN) framework, which guides LLMs in improving their performance by learning from previous mistakes.

clean-usnob Restricted orthogonal gradient projection for continual learning
Zeyuan Yang, Zonghan Yang, Yichen Liu, Peng Li, Yang Liu
AI Open, 2023
Paper | Code

In this work, we propose the Restricted Orthogonal Gradient prOjection (ROGO) framework. The basic idea is to adopt a restricted orthogonal constraint allowing parameters optimized in the direction oblique to the whole frozen space to facilitate forward knowledge transfer while consolidating previous knowledge.

clean-usnob Dynamic Multi-branch Layers for On-device Neural Machine Translation
Zhixing Tan, Zeyuan Yang, Meng Zhang, Qun Liu, Maosong Sun, Yang Liu
IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2022
Paper | Code

In this work, we propose to improve the performance of on-device NMT systems with dynamic multi-branch layers. Specifically, we design a layer-wise dynamic multi-branch network with only one branch activated during training and inference.

clean-usnob Exploring Deep Learning for Air Pollutant Emission Estimation
Lin Huang, Song Liu, Zeyuan Yang, Jia Xing, Jia Zhang, Jiang Bian, Siwei Li, Shovan Kumar Sahu, Shuxiao Wang, Tie-Yan Liu
Geoscientific Model Development, 2021
Paper

In this study, we proposed a novel method to model the dual relationship between an emission inventory and pollution concentrations for emission inventory estimation.

Internship

  • Zhiyuan Innovation Technology - Research Intern (Jan. 2024 - Aug. 2024)
    Manager: Prof. Hao Dong
  • Ruilai Wisdom Technology - Research Intern (Jan. 2021 - Jun. 2021)
    Manager: Shizhen Xu
  • Microsoft Research Asia - Research Intern (Jun. 2020 - Dec. 2020)
    Manager: Lin Huang, Jia Zhang
  • Natural Language Processing Group, JD AI Lab - Research Intern (Jan. 2020 - Jun. 2020)
    Manager: Meng Chen

Service

  • Conference Reviewer:
    ARR 2024, ICML 2025
  • Teaching Assistant:
    • Object-Oriented Programming, University of Massachusetts, Amherst (Fall 2024)
      Instructors: Prof. Jaime Davila, Cole Reilly
    • Foundations of Programming, University of Massachusetts, Amherst (Spring 2025)
      Instructors: Prof.Ghazaleh Parvini, Cole Reilly

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