Sijia Gu

SCATE: Learning to Supervise Coding Agents for Cost-Effective Test Generation

This work proposes SCATE, a framework for adaptive, automated supervision of coding agents that replaces human intervention during test generation by formulating supervision as a contextual bandit problem, which consistently outperforms state-of-the-art non-agentic approaches across all metrics.