Title: WebOperator: Action-Aware Tree Search for Autonomous Agents in Web Environment
Primary area: foundation or frontier models, including LLMs
Keywords: Web Agent, Web Navigation, Tree Search, LLM Agent

Abstract: Conventional WebAgents often operate in a greedy, step-by-step manner, selecting actions based solely on the current observation without considering long-term consequences or alternative paths. This lack of foresight is particularly problematic in web environments, which are only partially observable—limited to browser-visible content such as the current page’s DOM and UI elements—where a single misstep can drive the agent into a dead-end state, from where the goal is unreachable. Without a mechanism for backtracking, the agent has no way to correct such errors or explore alternative paths once a mistake has been made. Tree search methods provide a principled framework for such structured exploration, but existing approaches assume all actions are safe and reversible, ignore irreversible actions, and suffer from inefficient backtracking—limitations that reduce their effectiveness in realistic web tasks. To address these challenges, we introduce $\textbf{WebOperator}$, a tree-search framework for reliable and efficient exploration of web environments. Our method incorporates an action-aware Best-First Search strategy that ranks actions by both reward estimates and safety considerations, along with a robust backtracking mechanism that validates path feasibility through isolated simulation before committing changes to the main environment—preventing errors and minimizing redundant steps. In addition, WebOperator integrates strategies for guided exploration, experience retrieval that leverages past interactions, and semantic merging to eliminate redundant actions, enabling systematic completion of complex web tasks. Experiments on WebArena and WebVoyager show that even with weaker open-source backbone LLMs, our method outperforms current state-of-the-art approaches with proprietary strong models, demonstrating its effectiveness for autonomous agents in web environments.