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cs.AI2026
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,…
cs.AI2026
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…
cs.AI2025
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…