Papers by Qianhao Yuan
MemSearcher: Iterative Memory Integration for Search Agent via End-to-End Reinforcement Learning (2026.findings-acl)
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Qianhao Yuan, Jie Lou, Zichao Li, Jiawei Chen, Yaojie Lu, Hongyu Lin, Le Sun, Debing Zhang, Xianpei Han
| Challenge: | Recent LLM-based search agents often concatenate the full interaction history into the context, producing long and noisy inputs and increasing compute cost and memory overhead. |
| Approach: | They propose an agent framework that maintains a compact memory during multi-turn interactions. |
| Outcome: | The proposed framework outperforms strong history-concatenation (ReAct-style) baselines on a range of public datasets while maintaining nearly constant token counts across multi-turn interactions. |
DeepPresenter: Environment-Grounded Reflection for Agentic Presentation Generation (2026.findings-acl)
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Hao Zheng, Guozhao Mo, Xinru Yan, Qianhao Yuan, Wenkai Zhang, Xuanang Chen, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun
| Challenge: | Existing presentation agents rely on predefined workflows and fixed templates to generate presentations. |
| Approach: | They propose an agentic framework that adapts to diverse user intents and iterative refinement based on observation. |
| Outcome: | The proposed framework can be used to generate presentations with environmental observations. |
ConsistentChat: Building Skeleton-Guided Consistent Multi-Turn Dialogues for Large Language Models from Scratch (2025.emnlp-main)
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Jiawei Chen, Xinyan Guan, Qianhao Yuan, Mo Guozhao, Weixiang Zhou, Yaojie Lu, Hongyu Lin, Ben He, Le Sun, Xianpei Han
| Challenge: | Existing instruction data synthesis methods focus on single-turn instructions and neglect cross-turn coherence, resulting in context drift and reduced task completion rates. |
| Approach: | They propose a framework that constrains multi-turn instruction synthesis by explicitly modeling human conversational intent. |
| Outcome: | The proposed framework outperforms existing models trained on single-turn and multi-turn instruction datasets. |
ShortGPT: Layers in Large Language Models are More Redundant Than You Expect (2025.findings-acl)
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Xin Men, Mingyu Xu, Qingyu Zhang, Qianhao Yuan, Bingning Wang, Hongyu Lin, Yaojie Lu, Xianpei Han, Weipeng Chen
| Challenge: | Recent studies have identified significant redundancy in large language models . quantization and pruning are two methods that reduce computational resources . |
| Approach: | They propose simple pruning methods that prune redundant layers based on their BI scores. |
| Outcome: | The proposed pruning methods demonstrate superior performance over previous pruning methods. |