Papers by Jizhou Guo
LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey (2026.findings-acl)
Copied to clipboard
Henry Peng Zou, Wei-Chieh Huang, Yaozu Wu, Jizhou Guo, Yankai Chen, Chunyu Miao, Hoang H Nguyen, Yue Zhou, Weizhi Zhang, Liancheng Fang, Hanrong Zhang, Fangxin Wang, Pengfei Zhang, Langzhou He, Yangning Li, Dongyuan Li, Renhe Jiang, Philip S. Yu
| Challenge: | Recent advances in large language models (LLMs) have sparked growing interest in building fully autonomous agents. |
| Approach: | They propose to integrate human-provided information, feedback, or control into the agent system to enhance system performance, reliability, and safety. |
| Outcome: | The proposed systems improve system performance, reliability, and safety by integrating human-provided information, feedback, or control into the agent system. |
Model-based Large Language Model Customization as Service (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing large language model services require users to upload data for fine-tuning . current methods for customization are noisy and require sensitive domain data . |
| Approach: | *Llamdex is a framework that facilitates LLM customization as a service . client uploads pre-trained domain-specific *models* rather than data . |
| Outcome: | *Llamdex* framework improves domain-specific accuracy by up to 26% over state-of-the-art private data synthesis methods . |
Deep Research with Open-Domain Evaluation and Multi-Stage Guardrails for Safety (2026.acl-long)
Copied to clipboard
Wei-Chieh Huang, Henry Peng Zou, Yaozu Wu, Dongyuan Li, Yankai Chen, Weizhi Zhang, Yangning Li, Angelo Zangari, Jizhou Guo, Chunyu Miao, Liancheng Fang, Langzhou He, Yinghui Li, Renhe Jiang, Philip S. Yu
| Challenge: | Existing deep research frameworks lack adequate evaluation procedures and stage-specific protections. |
| Approach: | They propose a framework with open-domain evaluation and a stage-wise safety benchmark to address this oversight. |
| Outcome: | The proposed framework improves defense success rates by 16.53% while reducing over-refusal rates to approximately 6%. |