Agentic Test-Time Scaling for WebAgents

This work presents CATTS, a simple technique for dynamically allocating compute for multi-step agents, especially web agents. It empirically studies inference-time scaling for web agents, addressing the challenge of compounding errors in long-horizon, multi-step tasks and proposes solutions for improved reliability and performance in real-world web interaction.