WALT: Web Agents that Learn Tools

• URL: http://arxiv.org/abs/2510.01524v1
• Date: Wed, 01 Oct 2025 23:41:47 GMT
• Title: WALT: Web Agents that Learn Tools
• Authors: Viraj Prabhu, Yutong Dai, Matthew Fernandez, Jing Gu, Krithika Ramakrishnan, Yanqi Luo, Silvio Savarese, Caiming Xiong, Junnan Li, Zeyuan Chen, Ran Xu,
• Abstract summary: WALT is a framework that reverse-engineers latent website functionality into reusable invocable tools.<n>Rather than hypothesizing ad-hoc skills, WALT exposes robust implementations of automations already designed into websites.<n>On VisualWebArena and WebArena, WALT achieves higher success with fewer steps and less LLM-dependent reasoning.
• Score: 66.73502484310121
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Web agents promise to automate complex browser tasks, but current methods remain brittle -- relying on step-by-step UI interactions and heavy LLM reasoning that break under dynamic layouts and long horizons. Humans, by contrast, exploit website-provided functionality through high-level operations like search, filter, and sort. We introduce WALT (Web Agents that Learn Tools), a framework that reverse-engineers latent website functionality into reusable invocable tools. Rather than hypothesizing ad-hoc skills, WALT exposes robust implementations of automations already designed into websites -- spanning discovery (search, filter, sort), communication (post, comment, upvote), and content management (create, edit, delete). Tools abstract away low-level execution: instead of reasoning about how to click and type, agents simply call search(query) or create(listing). This shifts the computational burden from fragile step-by-step reasoning to reliable tool invocation. On VisualWebArena and WebArena, WALT achieves higher success with fewer steps and less LLM-dependent reasoning, establishing a robust and generalizable paradigm for browser automation.

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