Papers by Rui Pu
DSG-MCTS: A Dynamic Strategy-Guided Monte Carlo Tree Search for Diversified Reasoning in Large Language Models (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) have shown strong potential in complex reasoning tasks, but their performance often degrades, resulting in hallucinations, errors, and logical inconsistencies. |
| Approach: | They propose a framework that integrates multiple reasoning strategies to expand the reasoning space and a dynamic strategy selection mechanism that adapts to the task context. |
| Outcome: | The proposed framework outperforms existing state-of-the-art methods on a set of reasoning benchmarks. |
LoRAMoE: Alleviating World Knowledge Forgetting in Large Language Models via MoE-Style Plugin (2024.acl-long)
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Shihan Dou, Enyu Zhou, Yan Liu, Songyang Gao, Wei Shen, Limao Xiong, Yuhao Zhou, Xiao Wang, Zhiheng Xi, Xiaoran Fan, Shiliang Pu, Jiang Zhu, Rui Zheng, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Experimental results show that, as the instruction data increases, LoRAMoE can significantly improve the ability to process downstream tasks, while maintaining the world knowledge stored in the LLM. |
| Approach: | They propose a framework that introduces several low-rank adapters and integrates them by using a router network to freeze the backbone model and force a portion of LoRAs to focus on leveraging world knowledge to solve downstream tasks. |
| Outcome: | The proposed framework freezes the backbone model and forces a portion of LoRAs to focus on leveraging world knowledge to solve downstream tasks, to alleviate world knowledge forgetting. |
Beyond Single Frames: Can LMMs Comprehend Implicit Narratives in Comic Strip? (2025.findings-emnlp)
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Xiaochen Wang, Heming Xia, Jialin Song, Longyu Guan, Qingxiu Dong, Rui Li, Yixin Yang, Yifan Pu, Weiyao Luo, Yiru Wang, Xiangdi Meng, Wenjie Li, Zhifang Sui
| Challenge: | Large Multimodal Models have demonstrated strong performance on vision-language benchmarks, yet current evaluations focus on single-image reasoning. |
| Approach: | STRIPCIPHER is a benchmark designed to evaluate model ability on understanding implicit narratives in silent comics. |
| Outcome: | STRIPCIPHER is a high-quality, human-annotated dataset featuring fine-grained annotations and comprehensive coverage of varying difficulty levels. |
Beyond Surface-Level Detection: Towards Cognitive-Driven Defense Against Jailbreak Attacks via Meta-Operations Reasoning (2026.acl-long)
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| Challenge: | Existing defenses rely on shallow pattern matching, which struggles to generalize to novel and unseen attack strategies. |
| Approach: | They propose a framework which emulates human cognitive reasoning through a structured reasoning chain. |
| Outcome: | The proposed framework achieves state-of-the-art performance and exhibits strong generalization to unseen attacks. |
BaitAttack: Alleviating Intention Shift in Jailbreak Attacks via Adaptive Bait Crafting (2024.emnlp-main)
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| Challenge: | Existing attacks focus on meticulously constructing prompts to disguise harmful intentions . however, incorporation of disguising prompts may incur the challenge of "intention shift" |
| Approach: | They propose a jailbreak attack component, BaitAttack, to alleviate the effects of intention shift . Bait provides a response to the query, prompting LLMs to rectify or supplement the knowledge within the bait . |
| Outcome: | The proposed component, BaitAttack, reduces the effects of intention shift within jailbreak attacks. |
From "Aha Moments" to Controllable Thinking: Toward Meta-Cognitive Reasoning in LRMs via Decoupled Reasoning and Control (2026.acl-long)
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| Challenge: | Large Reasoning Models exhibit step-by-step reasoning, reflection, and backtracking, but these behaviors are often unregulated, leading to overthinking. |
| Approach: | They propose a meta-cognitive reasoning framework that decouples reasoning from control to enable independent optimization of control strategies. |
| Outcome: | Experiments show that the proposed model improves efficiency and accuracy across reasoning benchmarks. |