Papers by Qingsong Xie
UniEDU: Toward Unified and Efficient Large Multimodal Models for Educational Tasks (2025.emnlp-industry)
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| Challenge: | Existing research has focused on plain text, while real-world K-12 scenarios often involve multimodal data. |
| Approach: | They propose a unified language and vision assistant called UniEDU for educational applications . it excels across multiple educational tasks while maintaining strong generalization capabilities . authors propose to use UniEDu for industry-scale deployment . |
| Outcome: | The proposed model excels across multiple educational tasks while maintaining strong generalization capabilities. |
Diagnosing Failures in Large Language Models’ Answers: Integrating Error Attribution into Evaluation Framework (2025.findings-acl)
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| Challenge: | Existing evaluation models lack error attribution capability due to their proprietary nature. |
| Approach: | They propose a misattribution framework with 6 primary and 15 secondary categories to facilitate in-depth analysis. |
| Outcome: | The proposed framework is based on a dataset specifically designed for error attribution, along with the corresponding scores and feedback. |
ARM2: Adaptive Reasoning Model with Vision Understanding and Executable Code (2026.findings-acl)
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| Challenge: | Large Reasoning Models suffer from the "over-thinking" problem, causing performance degradation. |
| Approach: | They propose a unified model that balances reasoning performance and efficiency across multiple formats through a reinforcement learning framework augmented with length-aware optimization. |
| Outcome: | The proposed model reduces token costs while preserving performance compared to traditional models. |
LLM Agents for Education: Advances and Applications (2025.findings-emnlp)
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Zhendong Chu, Shen Wang, Jian Xie, Tinghui Zhu, Yibo Yan, Jingheng Ye, Aoxiao Zhong, Xuming Hu, Jing Liang, Philip S. Yu, Qingsong Wen
| Challenge: | Large Language Model (LLM) agents are transforming education by automating complex tasks and enhancing both teaching and learning processes. |
| Approach: | This survey analyzes recent advances in applying Large Language Model agents to educational settings . it highlights ethical issues, hallucination and overreliance, and integration with existing ecosystems . |
| Outcome: | The authors analyze the technologies enabling LLM agents and highlight key challenges in deploying them in educational settings. |
DaMo: Data Mixing Optimizer in Fine-tuning Multimodal LLMs for Mobile Phone Agents (2026.findings-acl)
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Kai Shi, Jun Yang, Ni Yang, Binqiang Pan, Qingsong Xie, null Zhangchao, Zhenyu Yang, Tianhuang Su, Haonan Lu
| Challenge: | Mobile Phone Agents (MPAs) have attracted huge attention due to their practicability in a multitude of scenarios. |
| Approach: | They propose a data mixture optimization solution that extrapolates optimal data mixtures from a trainable network. |
| Outcome: | The proposed model outperforms existing methods on open-source benchmarks and on open source benchmarks. |