Papers by Hanghui Guo
ACR: Adaptive Context Refactoring via Context Refactoring Operators for Multi-Turn Dialogue (2026.findings-acl)
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Jiawei Shen, Jia Zhu, Hanghui Guo, Weijie Shi, Yue Cui, Qingyu Niu, Guoqing Ma, Jingjiang Liu, Yidan Liang, Yilin Wang, Shimin Di, Jiajie Xu
| Challenge: | Existing approaches to multi-turn dialogues lack contextual consistency and dependencies, and models struggle to maintain factual faithfulness as interaction turns increase. |
| Approach: | They propose an adaptive context refactoring framework that monitors and reshapes the interaction history to mitigate contextual inertia and state drift. |
| Outcome: | The proposed model outperforms baselines while reducing token consumption. |
RSDA: Restoring Stale Data Affinity via Dynamic Renovation Strategy for Mitigating Data Scarcity (2026.acl-long)
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Yidan Liang, Jia Zhu, Weijie Shi, Hanghui Guo, Yue Cui, Jiawei Shen, Guoqing Ma, Jingjiang Liu, Qingyu Niu, Yilin Wang, Shimin Di, Jiajie Xu
| Challenge: | High-quality data is the cornerstone of advancing large language models, but the supply of premium data is nearing depletion, while vast stale corpora remain underutilized. |
| Approach: | They propose a framework to restore stale data affinity by quantifying the latent value of samples and employing a dynamic renovation strategy selection mechanism to determine the optimal component-level strategy. |
| Outcome: | The proposed framework achieves performance improvements using less than 10% of the data volume, underscoring that the latent potential of stale corpora remains largely untapped. |
KCVR: Knowledge-Centric Video Reconstruction for Structured Pedagogical Summarization via Dynamic Graph Planning (2026.acl-long)
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Jingjiang Liu, Jia Zhu, Hanghui Guo, Weijie Shi, Yue Cui, Xiaokang Jin, Yilin Wang, Qingyu Niu, Jiawei Shen, Guoqing Ma, Yidan Liang, Shimin Di, Jiajie Xu
| Challenge: | Existing summarization methods compress content for gist browsing, but they break prerequisite logic in instructional videos. |
| Approach: | They propose a framework that decouples epistemic planning from content generation. |
| Outcome: | The proposed framework outperforms strong end-to-end baselines on Knowledge Progression Consistency and Learning Objective Coverage. |
DioR: Adaptive Cognitive Detection and Contextual Retrieval Optimization for Dynamic Retrieval-Augmented Generation (2025.acl-long)
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| Challenge: | Existing methods for generating large language models face limitations in key aspects such as retrieval triggers and contextual scrutiny of retrieval content. |
| Approach: | They propose a dynamic RAG method that uses cognitive detection and contextual retrieval optimization to determine when retrieval is needed and what to retrieve for LLMs. |
| Outcome: | The proposed method achieves superior performance on all tasks, demonstrating the effectiveness of the proposed method. |