Papers by Gong Zhi
KAPA: A Deliberative Agent Framework with Tree-Structured Knowledge Base for Multi-Domain User Intent Understanding (2025.findings-acl)
Copied to clipboard
Jiakai Tang, Shiqi Shen, ZhipengWang ZhipengWang, Gong Zhi, Xueyang Feng, Zexu Sun, Haoran Tan, Xu Chen
| Challenge: | Existing studies on the use of LLMs for estimating user intents are either too far from real human thought processes or require labeled samples. |
| Approach: | They propose a deliberative agent framework that leverages human thought process to build high-level domain knowledge and a tree-structured knowledge base to store refined experience and data. |
| Outcome: | The proposed framework is able to build high-level domain knowledge and efficiently store it across multiple steps. |
Towards Tool Use Alignment of Large Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies on tool use with LLMs focus on enhancing tool-calling ability of LLM . e.g., LLM should not answer unsafe tool use relevant instructions or insecure tool responses to ensure reliability and harmlessness. |
| Approach: | They propose to use supervised fine-tuning and preference learning to align LLMs with H2A principle for tool use. |
| Outcome: | The proposed model demonstrates that LLMs can generate truthful and helpful responses while remaining harmless. |
GUI0: Self-Evolving Foundational GUI Agents in Super App Ecosystems (2026.acl-long)
Copied to clipboard
Xinyi Wang, Wei Dai, Kyle Qiao, Ke Wang, Peng Chen, Gang Cao, null Kangqin, Zhongpu Wang, Xiaode Zhang, Yanming Liu, Jihao Gu, Jingtao Xu, Gong Zhi
| Challenge: | Automated interaction with graphical user interfaces (GUIs) is central to general artificial intelligence, but remains challenging within Super App ecosystems. |
| Approach: | They propose a framework synergizing autonomous data synthesis with dual-agent co-evolution . GUI0 establishes a domain-aware foundation model via synthesized corpora and employs curriculum-driven reinforcement learning . |
| Outcome: | The proposed framework outperforms Gemini-2.5-Pro and Claude-4-Sonnet in the SuperAPP benchmark and has universal efficacy across base models. |