Papers by Mingyu Luo
Disentangling Memory and Reasoning Ability in Large Language Models (2025.acl-long)
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Mingyu Jin, Weidi Luo, Sitao Cheng, Xinyi Wang, Wenyue Hua, Ruixiang Tang, William Yang Wang, Yongfeng Zhang
| Challenge: | Existing LLMs operate as an opaque process without explicit separation between knowledge retrieval and reasoning steps, making the decision-making process unclear and disorganized. |
| Approach: | They propose a language model inference paradigm that decomposes the complex inference process into two distinct and clear actions: (1) memory recall: which retrieves relevant knowledge, and (2) reasoning: which performs reasoning steps based on the recalled knowledge. |
| Outcome: | The proposed paradigm decomposes the inference process into two distinct and clear actions, memory and reason, guiding the model to distinguish between steps that require knowledge retrieval and those that involve reasoning. |
BattleAgent: Multi-modal Dynamic Emulation on Historical Battles to Complement Historical Analysis (2024.emnlp-demo)
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Shuhang Lin, Wenyue Hua, Lingyao Li, Che-Jui Chang, Lizhou Fan, Jianchao Ji, Hang Hua, Mingyu Jin, Jiebo Luo, Yongfeng Zhang
| Challenge: | Recent advances in large language models have demonstrated impressive reasoning capabilities, indicating their potential to serve as the foundation for agents. |
| Approach: | They propose a detailed emulation system that combines large vision-language model and multi-agent system to emulate dynamic interactions between multiple agents over a period of time. |
| Outcome: | The proposed system combines large vision-language model and multi-agent system to emulate dynamic interactions between agents and their environments over a period of time. |
RevPRAG: Revealing Poisoning Attacks in Retrieval-Augmented Generation through LLM Activation Analysis (2025.findings-emnlp)
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| Challenge: | Retrieval-Augmented Generation (RAG) enriches the input to LLMs by retrieving information from the relevant knowledge database. |
| Approach: | They propose to use a knowledge database to enrich the input of LLMs by retrieving information from the relevant knowledge database. |
| Outcome: | The proposed approach can achieve 98% true positive rate while maintaining a false positive rate close to 1%. |