Papers by Shaokun Zhang
Divide, Optimize, Merge: Scalable Fine-Grained Generative Optimization for LLM Agents (2025.findings-emnlp)
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Jiale Liu, Yifan Zeng, Shaokun Zhang, Chi Zhang, Malte Højmark-Bertelsen, Marie Normann Gadeberg, Huazheng Wang, Qingyun Wu
| Challenge: | LLM-based generative optimization has shown remarkable potential in improving agentic systems, but the current approach of prompting with the trajectories on the whole training dataset becomes untenable as datasets grow. |
| Approach: | They propose a scalable framework that divides large optimization tasks into manageable subsets and performs targeted optimizations. |
| Outcome: | The proposed framework outperforms conventional approach by 1.6-8.6% while reducing average prompt token consumption by 56.3%. |
Chinese Court Simulation with LLM-Based Agents System (2026.findings-acl)
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Kaiyuan Zhang, Jiaqi Li, Yueyue Wu, Haitao Li, Cheng Luo, Shaokun Zou, Yujia Zhou, Weihang Su, Yiqun Liu, Qingyao Ai
| Challenge: | Existing studies have neglected the systematic design and procedure evaluation of court simulations, which are critical to the credibility and usage of court simulators in practice. |
| Approach: | They propose a court simulation paradigm based on the real-world procedure structure of Chinese courts and a framework that focuses on both legal judgment prediction and court procedure analysis. |
| Outcome: | The proposed model outperforms judges and lawyers from the real trials in many aspects. |