Papers by Xin Cong
RepoAgent: An LLM-Powered Open-Source Framework for Repository-level Code Documentation Generation (2024.emnlp-demo)
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Qinyu Luo, Yining Ye, Shihao Liang, Zhong Zhang, Yujia Qin, Yaxi Lu, Yesai Wu, Xin Cong, Yankai Lin, Yingli Zhang, Xiaoyin Che, Zhiyuan Liu, Maosong Sun
| Challenge: | Xia et al., 2018) demonstrate that a large language model can generate and maintain high-quality code documentation. |
| Approach: | They propose a large language model powered open-source framework for generating, maintaining, and updating code documentation. |
| Outcome: | The proposed framework generates high-quality documentation for the entire project. |
Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agents (2024.acl-long)
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Cheng Qian, Bingxiang He, Zhong Zhuang, Jia Deng, Yujia Qin, Xin Cong, Zhong Zhang, Jie Zhou, Yankai Lin, Zhiyuan Liu, Maosong Sun
| Challenge: | Current language model-driven agents lack mechanisms for effective user participation, which is crucial given the vagueness commonly found in user instructions. |
| Approach: | They propose a benchmark to inspect users’ implicit intentions through explicit queries and a model expert as the upstream in agent design to enhance user-agent interaction. |
| Outcome: | The proposed approach excels at identifying vague user tasks, recovering and summarizing critical missing information, setting precise and necessary agent execution goals, and minimizing redundant tool usage, thus boosting overall efficiency. |
AgentCPM-GUI: Building Mobile-Use Agents with Reinforcement Fine-Tuning (2025.emnlp-demos)
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Zhong Zhang, Yaxi Lu, Yikun Fu, Yupeng Huo, Shenzhi Yang, Yesai Wu, Han Si, Xin Cong, Haotian Chen, Yankai Lin, Xie Xie, Wei Zhou, Wang Xu, Zhou Su, Zhongwu Zhai, Xiaoming Liu, null Meiyudong, Jianming Xu, Hongyan Tian, Chongyi Wang, Chi Chen, Yuan Yao, Zhiyuan Liu, Maosong Sun
| Challenge: | Large language model agents have enabled GUI-based automation, but their deployment is limited by noisy data, poor generalization, and lack of support for non-English GUIs. |
| Approach: | They propose an 8B-parameter GUI agent built for robust and efficient on-device GUI interaction. |
| Outcome: | The proposed GUI agent achieves promising performance on five public benchmarks and proposed Chinese benchmark CAGUI. |
Event Causality Extraction with Event Argument Correlations (2022.coling-1)
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| Challenge: | Event Causality Identification (ECI) ignores crucial event structure and cause-effect component information, making it struggle for downstream applications. |
| Approach: | They propose a task to extract event causality pairs with their structured event information from plain text. |
| Outcome: | The proposed method captures the intra- and inter-event argument correlations for ECE and provides several future directions. |
Enhancing Chinese Pre-trained Language Model via Heterogeneous Linguistics Graph (2022.acl-long)
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| Challenge: | Experimental results show that pre-trained Chinese language models ignore linguistics knowledge to learn representations. |
| Approach: | They propose a task-free enhancement module to integrate linguistics knowledge into Chinese pre-trained language models. |
| Outcome: | The proposed model improves Chinese pre-trained language models on 6 tasks with 10 benchmark datasets. |
MatPlotAgent: Method and Evaluation for LLM-Based Agentic Scientific Data Visualization (2024.findings-acl)
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Zhiyu Yang, Zihan Zhou, Shuo Wang, Xin Cong, Xu Han, Yukun Yan, Zhenghao Liu, Zhixing Tan, Pengyuan Liu, Dong Yu, Zhiyuan Liu, Xiaodong Shi, Maosong Sun
| Challenge: | Scientific data visualization is an essential process in research, but its use of large language models remains unexplored. |
| Approach: | They propose a model-agnostic LLM agent framework to automate scientific data visualization tasks. |
| Outcome: | The proposed framework improves performance of commercial and open-source models. |
SimPBL: A Multi-Agent Framework for Project-Based Learning (2026.acl-long)
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Daniel Zhang-Li, Joy Jia Yin Lim, Binglin Liu, Shangqing Tu, Zijun Yao, Hao Peng, Jifan Yu, Haoxuan Li, Zhanxin Hao, Ye He, Zekun Li, Jiangyi Wang, Lei Hou, Bin Xu, Xin Cong, Zhiyuan Liu, Huiqin Liu, Yu Zhang, Juanzi Li
| Challenge: | Existing LLMs provide partial assistance without modeling these roles, and overly comprehensive help can reduce learner autonomy. |
| Approach: | They propose a multi-agent framework with an orchestrator agent that provides adaptive scaffolding from interaction logs and collaborator agents that support project work through boundary-aware collaboration. |
| Outcome: | The proposed framework improves learner examination scores by 14% . it is based on a multi-agent framework with an orchestrator agent . |
Enhanced Language Representation with Label Knowledge for Span Extraction (2021.emnlp-main)
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| Challenge: | Existing approaches to extract text spans from plain text do not fully exploit label knowledge. |
| Approach: | They propose a model to integrate label knowledge into text representations by encoding texts and annotations independently and then integrating label knowledge with an elaborate-designed semantics fusion module. |
| Outcome: | The proposed model achieves state-of-the-art performance on four benchmarks and reduces training time and inference time by 76% and 77% on average compared with the existing paradigm. |
AgentRM: Enhancing Agent Generalization with Reward Modeling (2025.acl-long)
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Yu Xia, Jingru Fan, Weize Chen, Siyu Yan, Xin Cong, Zhong Zhang, Yaxi Lu, Yankai Lin, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing LLM-based agents have strong performance on held-in tasks, but their generalizability to unseen tasks remains poor. |
| Approach: | They propose a reward-based generalizable reward model to guide the policy model for effective test-time search. |
| Outcome: | The proposed agentRM outperforms existing agents on held-in tasks by 8.8 points on average. |
From Scaffolding to Assimilation: Progressive Structural Internalization for Format-Constrained Creative Text Generation (2026.findings-acl)
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| Challenge: | Existing paradigms rely on unreliable prompting or rigid constrained decoding strategies to achieve aesthetic unity. |
| Approach: | They propose a framework to embed external constraints into the model’s intrinsic intuition and use it to generate open-ended creative texts. |
| Outcome: | The proposed framework surpasses baselines in both strict constraint adherence and literary aesthetics. |
Distance between Relevant Information Pieces Causes Bias in Long-Context LLMs (2025.findings-acl)
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Runchu Tian, Yanghao Li, Yuepeng Fu, Siyang Deng, Qinyu Luo, Cheng Qian, Shuo Wang, Xin Cong, Zhong Zhang, Yesai Wu, Yankai Lin, Huadong Wang, Xiaojiang Liu
| Challenge: | Positional biases in large language models hinder their ability to process long inputs. |
| Approach: | They propose a benchmark to assess positional bias in large language models involving multiple pieces of relevant information. |
| Outcome: | The proposed benchmark assesses the performance of long-context language models by examining their models with different input lengths and tasks. |
Universal Information Extraction with Meta-Pretrained Self-Retrieval (2023.findings-acl)
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Xin Cong, Bowen Yu, Mengcheng Fang, Tingwen Liu, Haiyang Yu, Zhongkai Hu, Fei Huang, Yongbin Li, Bin Wang
| Challenge: | Existing methods for IE are task-specific, resulting in specialized and isolated approaches for different tasks. |
| Approach: | They propose a method to retrieve task-specific knowledge from pretrained language models to enhance universal IE by using a Meta-Pretraining Algorithm. |
| Outcome: | The proposed method achieves the new state-of-the-art on 4 IE tasks, 12 datasets under fully-supervised, low-resource and few-shot scenarios. |
Few-Shot Event Detection with Prototypical Amortized Conditional Random Field (2021.findings-acl)
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| Challenge: | Existing approaches to event detection ignore the trigger discrepancy and cause errors. |
| Approach: | They propose a unified model which converts a few-shot tagging problem into a single-shot model by using a Gaussian distribution. |
| Outcome: | The proposed model performs better than existing identifythen-classify models on a few-shot tagging problem with a double-part taging scheme. |
Experiential Co-Learning of Software-Developing Agents (2024.acl-long)
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Chen Qian, Yufan Dang, Jiahao Li, Wei Liu, Zihao Xie, YiFei Wang, Weize Chen, Cheng Yang, Xin Cong, Xiaoyin Che, Zhiyuan Liu, Maosong Sun
| Challenge: | Recent advances in large language models (LLMs) have brought significant changes to various domains, especially through autonomous agents. |
| Approach: | They propose a framework that lets agents learn shortcuts from their past tasks and use them for future task execution. |
| Outcome: | The proposed framework enables agents to tackle unseen software-developing tasks more effectively. |
Learning to Generate Structured Output with Schema Reinforcement Learning (2025.acl-long)
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Yaxi Lu, Haolun Li, Xin Cong, Zhong Zhang, Yesai Wu, Yankai Lin, Zhiyuan Liu, Fangming Liu, Maosong Sun
| Challenge: | Recent advances in large language models have facilitated the development of intelligent applications like automatic web search (Qin et al., 2023) Several methods exist for generating JSON strings from LLMs, including Prompting but often miss certain schemas. |
| Approach: | They propose to use 40K different JSON schemas to assess models' ability to generate valid JSON outputs. |
| Outcome: | The proposed model improves both in generating JSON outputs and downstream tasks. |
DebugBench: Evaluating Debugging Capability of Large Language Models (2024.findings-acl)
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Runchu Tian, Yining Ye, Yujia Qin, Xin Cong, Yankai Lin, Yinxu Pan, Yesai Wu, Hui Haotian, Liu Weichuan, Zhiyuan Liu, Maosong Sun
| Challenge: | Large language models (LLMs) have demonstrated exceptional coding capabilities, but their debugging capabilities remain relatively unexplored. |
| Approach: | They propose a debugging benchmark consisting of 4,253 LLMs with four major bug categories and 18 minor types in C++, Java, and Python. |
| Outcome: | The proposed benchmark covers four major bug categories and 18 minor types in C++, Java, and Python. |
Enhancing Open-Domain Task-Solving Capability of LLMs via Autonomous Tool Integration from GitHub (2025.acl-long)
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Bohan Lyu, Xin Cong, Heyang Yu, Pan Yang, Cheng Qian, Zihe Wang, Yujia Qin, Yining Ye, Yaxi Lu, Chen Qian, Zhong Zhang, Yukun Yan, Yankai Lin, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing approaches lack flexibility to address diverse and ever-evolving user queries in open domains. |
| Approach: | They propose to evaluate LLMs on open-domain knowledge that requires tools to solve diverse and ever-evolving user queries. |
| Outcome: | The proposed system outperforms baselines in the open domain task-solving benchmark. |