4 papers
AgentTrust: A Self-Improving Trust Layer for AI-Agent Actions
Chenglin Yang
AI agents increasingly take consequential actions -- shell commands, cloud operations, and arbitrary tool-calls -- so a trust layer must decide, per action, whether to allow, warn,…
AgentTrust: Runtime Safety Evaluation and Interception for AI Agent Tool Use
Chenglin Yang
Modern AI agents execute real-world side effects through tool calls such as file operations, shell commands, HTTP requests, and database queries. A single unsafe action, including…
A Survey of Large Language Models
Wayne Xin Zhao, Kun Zhou, Junyi Li +19
Language is essentially a complex, intricate system of human expressions governed by grammatical rules. It poses a significant challenge to develop capable AI algorithms for compre…
ToolMind Technical Report: A Large-Scale, Reasoning-Enhanced Tool-Use Dataset
Chen Yang, Ran Le, Yun Xing +5
Large Language Model (LLM) agents have developed rapidly in recent years to solve complex real-world problems using external tools. However, the scarcity of high-quality trajectori…