Papers by Jingxiang Qu
RL-Guider: Leveraging Historical Decisions and Feedback for Drug Editing with Large Language Models (2025.findings-acl)
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| Challenge: | RLs can be used to refine drugs by iterative conversations with domain experts . existing methods do not leverage past knowledge, but human experts develop intuition over time through historical experience . |
| Approach: | They propose a reinforcement-learning agent to provide suggestions to large language models . RL-Guider leverages the “world-level” knowledge of LLMs and historical feedback . |
| Outcome: | a new reinforcement-learning agent improves the performance of large language models . the proposed agent leverages the “world-level” knowledge of LLMs and historical feedback . |
Evaluating Memory Capability in Continuous Lifelog Scenario (2026.findings-acl)
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Jianjie Zheng, Zhichen Liu, Zhanyu Shen, Jingxiang Qu, Guanhua Chen, Yile Wang, Yang Xu, Yang Liu, Sijie Cheng
| Challenge: | Existing benchmarks focus on online one-on-one chatting or human-AI interactions, neglecting real-world scenarios. |
| Approach: | They propose a framework to curate a lifelog benchmark that combines two subsets of audio data to address temporal leakage in offline settings. |
| Outcome: | The proposed framework outperforms existing benchmarks on live chats and AI interactions. |