Papers by Qingqing Sun
Thought-Action Graph Reasoning: Faithful and Efficient Reasoning of Large Language Models via Reusing Past Experience (2026.findings-acl)
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Zhixiao Qi, Feng Huang, Yunqi Zhang, Shijie Zhang, Qingqing Sun, Yongfeng Huang, Minghu Jiang, Shuai Chen, Tianyi Zhang
| Challenge: | Existing methods for integrating knowledge graphs with LLMs suffer from poor generalization or low reasoning efficiency. |
| Approach: | They propose a thought-action Graph (TAG) that decomposes LLM-KG interaction trajectories into fine-grained semantic operators and guides LLM to execute on them. |
| Outcome: | The proposed paradigm outperforms state-of-the-art methods on KGQA benchmarks while reducing the number of LLM calls and generated tokens. |
RISK: A Framework for GUI Agents in E-commerce Risk Management (2026.acl-long)
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Renqi Chen, Zeyin Tao, Jianming Guo, Jingzhe Zhu, Yiheng Peng, Qingqing Sun, Tianyi Zhang, Shuai Chen
| Challenge: | RISK is a framework designed to automate multi-step web interactions in e-commerce risk management. |
| Approach: | a new framework is designed to build and deploy GUI agents for e-commerce risk management . RISK-R1 provides a scalable, domain-specific solution for automating complex web interactions . |
| Outcome: | RISK provides a scalable, domain-specific solution for automating complex web interactions in e-commerce risk management. |
A Survey on Multi-modal Intent Recognition: Recent Advances and New Frontiers (2025.findings-emnlp)
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| Challenge: | Multi-modal intent recognition (MIR) requires integrating non-verbal cues from real-world contexts to enhance human intention understanding. |
| Approach: | They present a comprehensive review of multi-modal intent recognition . they provide a survey of the field covering textual, visual, and acoustic signals . |
| Outcome: | The present survey summarises the current state of multi-modal intent recognition . it includes a comprehensive taxonomy and advanced methods . |