Author name cluster

Qiuying Peng

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5 papers

2 author rows

ECAI Conference 2025 Conference Paper

Diversity-Aware Self-Paced Data Selection for LLM Fine-Tuning

Yingxuan Yang
Huayi Wang
Muning Wen
Xiaoyun Mo
Qiuying Peng
Jun Wang 0020
Weinan Zhang 0001

Fine-tuning large language models (LLMs) is challenged by the presence of noisy data and the high computational cost when training on large-scale datasets. While data selection has emerged as a promising approach to reduce training cost and improve data quality, existing methods often rely on static heuristics or manual metrics. These approaches struggle to adapt to the model’s evolving capabilities during training, as its understanding of tasks improves. As the model becomes more powerful, its requirements for data that can enhance performance also change, making it crucial to incorporate this dynamic into the data selection process. Moreover, ensuring data diversity throughout different stages of training is essential for preventing redundancy, reducing overfitting. To address these issues, we propose DSP, a Diversity-Aware Self-Paced data selection framework that evolves with the model. DSP progressively selects training samples based on the model’s own outputs and incorporates a diversity-aware mechanism to enhance generalization and mitigate overfitting. Unlike prior static or rule-based strategies, DSP adaptively adjusts to the model’s internal feedback and training stage. Experiments on two public benchmarks demonstrate that DSP consistently outperforms static and heuristic-based baselines across multiple datasets and backbone models. Our findings highlight the critical role of dynamic, diversity-aware data selection in effective LLM fine-tuning.

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NeurIPS Conference 2025 Conference Paper

MobileUse: A Hierarchical Reflection-Driven GUI Agent for Autonomous Mobile Operation

Ning Li
Xiangmou Qu
Jiamu Zhou
Muning Wen
Kounianhua Du
Xingyu Lou
Qiuying Peng
Jun Wang

Recent advances in Multimodal Large Language Models (MLLMs) have enabled the development of mobile agents that can understand visual inputs and follow user instructions, unlocking new possibilities for automating complex tasks on mobile devices. However, applying these models to real-world mobile scenarios remains a significant challenge due to the long-horizon task execution, difficulty in error recovery, and the cold-start problem in unfamiliar environments. To address these challenges, we propose MobileUse, a GUI agent designed for robust and adaptive mobile task execution. To improve resilience in long-horizon tasks and dynamic environments, we introduce a hierarchical reflection architecture that enables the agent to self-monitor, detect, and recover from errors across multiple temporal scales—ranging from individual actions to overall task completion—while maintaining efficiency through a Reflection-on-Demand strategy. To tackle cold-start issues, we further introduce a proactive exploration module, which enriches the agent’s understanding of the environment through self-planned exploration. Evaluations on the AndroidWorld and AndroidLab benchmarks demonstrate that MobileUse establishes new state-of-the-art performance, achieving success rates of 62. 9% and 44. 2%, respectively. To facilitate real-world applications, we release an out-of-the-box toolkit for automated task execution on physical mobile devices, which is available at https: //github. com/MadeAgents/mobile-use.

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ICLR Conference 2025 Conference Paper

Robust Function-Calling for On-Device Language Model via Function Masking

Qiqiang Lin
Muning Wen
Qiuying Peng
Guanyu Nie
Junwei Liao
Xiaoyun Mo
Jiamu Zhou
Cheng Cheng

Large language models have demonstrated impressive value in performing as autonomous agents when equipped with external tools and API calls. Nonetheless, effectively harnessing their potential for executing complex tasks crucially relies on enhancements in their function-calling capabilities. This paper identifies a critical gap in existing function-calling models, where performance varies significantly across benchmarks, often due to over-fitting to specific naming conventions. To address such an issue, we introduce Hammer, a novel family of foundation models specifically engineered for on-device function calling. Hammer employs an augmented dataset that enhances models’ sensitivity to irrelevant functions and incorporates function masking techniques to minimize over-fitting. Our empirical evaluations reveal that Hammer not only outperforms larger models but also demonstrates robust generalization across diverse benchmarks, achieving state-of-the-art results. Our open-source contributions include a specialized dataset for irrelevance detection, a tuning framework for enhanced generalization, and the Hammer models, establishing a new standard for function-calling performance.

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AAMAS Conference 2025 Conference Paper

Unlocking the Potential of Decentralized LLM-based MAS: Privacy Preservation and Monetization in Collective Intelligence

Yingxuan Yang
Qiuying Peng
Jun Wang
Ying Wen
Weinan Zhang

Recent advances in large language models (LLMs) have enabled the development of LLM agents—autonomous systems capable of perceiving their environment, reasoning about tasks, and taking actions using external tools. While existing LLM-based Multi-Agent Systems (LaMAS) have shown promising results, they are predominantly centralized, operating within specific tasks or scenarios. These centralized designs simplify coordination but are fundamentally constrained by the limited data and knowledge available within a single entity. As LLM agents see broader deployment, the complexity of tasks increasingly requires collaboration across multiple organizations and data domains. Since organizations cannot and will not fully share their proprietary data, the next frontier of artificial intelligence lies in collective intelligence through decentralized LLM-based Multi-Agent Systems (LaMAS), where LLM agents, each accessing proprietary knowledge and tools, collaborate to solve complex tasks. This paradigm is becoming not just possible but necessary with the growing adoption of LLM agents across diverse organizations. This paper explores the transformative potential of decentralized LaMAS. In decentralized settings, two key issues arise: (1) privacy-preserving mechanisms that enable meaningful collaboration while safeguarding proprietary data and knowledge, and (2) monetization and credit attribution mechanisms that incentivize continuous improvement of agent capabilities and ensure fair value distribution among participants. Our analysis reveals that addressing these challenges can unlock a new paradigm of artificial collective intelligence that overcomes the limitations. This work contributes to decentralized AI by proposing a practical framework for mechanism design that advances both technological innovation and economic sustainability in decentralized LLM Agent networks.

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IJCAI Conference 2023 Conference Paper

Graph Propagation Transformer for Graph Representation Learning

Zhe Chen
Hao Tan
Tao Wang
Tianrun Shen
Tong Lu
Qiuying Peng
Cheng Cheng
Yue Qi

This paper presents a novel transformer architecture for graph representation learning. The core insight of our method is to fully consider the information propagation among nodes and edges in a graph when building the attention module in the transformer blocks. Specifically, we propose a new attention mechanism called Graph Propagation Attention (GPA). It explicitly passes the information among nodes and edges in three ways, i. e. node-to-node, node-to-edge, and edge-to-node, which is essential for learning graph-structured data. On this basis, we design an effective transformer architecture named Graph Propagation Transformer (GPTrans) to further help learn graph data. We verify the performance of GPTrans in a wide range of graph learning experiments on several benchmark datasets. These results show that our method outperforms many state-of-the-art transformer-based graph models with better performance. The code will be released at https: //github. com/czczup/GPTrans.

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