Token Efficient Task Execution via Application Behavior Modeling for Web Agents

• URL: http://arxiv.org/abs/2609.13491v1
• Date: Fri, 11 Sep 2026 19:57:22 GMT
• Title: Token Efficient Task Execution via Application Behavior Modeling for Web Agents
• Abstract summary: OdoBot is a novel web-agent architecture that completes tasks at a fraction of the cost when compared to conventional web agents.<n>Our experiments with 45 tasks on the Canvas Learning Management System (LMS) demonstrate that OdoBot uses 44% and 80% fewer tokens than two state-of-the-art competitor agents.
• Score: 47.04428350077317
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The strong performance of AI Agents across an impressive variety of tasks is driving an unprecedented investment in agentic infrastructures, however the cost of processing tokens is fast increasing. Web agents automate the execution of web-application tasks described in natural language, by analyzing the web-application's user interface (UI) and interacting with it. This work introduces OdoBot, a novel web-agent architecture that completes tasks at a fraction of the cost when compared to conventional web agents. This is achieved by leveraging a behavioral model of the underlying application constructed by analyzing successful task-execution demonstrations. Our experiments with 45 tasks on the Canvas Learning Management System (LMS) demonstrate that OdoBot uses 44% and 80% fewer tokens than two state-of-the-art competitor agents (Agent-E and WebVoyager), while also surpassing WebVoyager in terms of task success rate.

Related papers

• CAP: A Scalable Benchmark for Evaluating Cross-Site Browser Agents with Complex Actions and Perception [22.620664364045286]
  Large language models are increasingly deployed as autonomous agents that interact with the web through browsers.<n>We introduce CAP, a scalable benchmark for evaluating browser agents on cross-site, human-like web tasks.<n>We construct 420 tasks across 108 real-world websites and 24 domains under careful quality control.
  arXiv  Detail & Related papers  (2026-08-09T01:08:19Z)
• GTA: Generating Long-Horizon Tasks for Web Agents at Scale [82.43869456830664]
  We introduce a scalable framework, GTA, that integrates crawling, retrieval-based seeding, in-context generation, and automated quality control.<n>We instantiate the pipeline on over 50 websites covering e-commerce, government, forums, and news, with multilingual and multi-hop coverage.<n>Our contributions are three-fold: (i) formalizing multi-hop web-agent task generation, (ii) proposing an efficient and validated pipeline for automatic data creation, and (iii) releasing a dynamic benchmark with reproducible evaluation.
  arXiv  Detail & Related papers  (2026-05-28T01:05:50Z)
• It's a TRAP! Task-Redirecting Agent Persuasion Benchmark for Web Agents [52.81924177620322]
  Web-based agents powered by large language models are increasingly used for tasks such as email management or professional networking.<n>Their reliance on dynamic web content makes them vulnerable to prompt injection attacks: adversarial instructions hidden in interface elements that persuade the agent to divert from its original task.<n>We introduce the Task-Redirecting Agent Persuasion Benchmark (TRAP), an evaluation for studying how persuasion techniques misguide autonomous web agents on realistic tasks.
  arXiv  Detail & Related papers  (2025-12-29T01:09:10Z)
• Beyond Browsing: API-Based Web Agents [58.39129004543844]
  API-Based Agents outperform web Browsing Agents in experiments on WebArena.<n>Hybrid Agents out-perform both others nearly uniformly across tasks.<n>Results strongly suggest that when APIs are available, they present an attractive alternative to relying on web browsing alone.
  arXiv  Detail & Related papers  (2024-10-21T19:46:06Z)
• Steward: Natural Language Web Automation [19.301371856154965]
  Large language models (LLMs) have demonstrated exceptional capabilities in serving as the foundation for AI assistants. We introduce Steward, a novel LLM-powered web automation tool designed to serve as a cost-effective, scalable, end-to-end solution for automating web interactions. We discuss various design and implementation challenges, including state representation, action sequence selection, system responsiveness, detecting task completion, and caching implementation.
  arXiv  Detail & Related papers  (2024-09-23T18:06:32Z)
• WebSuite: Systematically Evaluating Why Web Agents Fail [2.200477647229223]
  We describe WebSuite, the first diagnostic benchmark for generalist web agents. This benchmark suite consists of both individual tasks, such as clicking a button, and end-to-end tasks, such as adding an item to a cart. We evaluate two popular generalist web agents, one text-based and one multimodal, and identify unique weaknesses for each agent.
  arXiv  Detail & Related papers  (2024-06-01T00:32:26Z)
• WebVoyager: Building an End-to-End Web Agent with Large Multimodal Models [65.18602126334716]
  Existing web agents typically only handle one input modality and are evaluated only in simplified web simulators or static web snapshots. We introduce WebVoyager, an innovative Large Multimodal Model (LMM) powered web agent that can complete user instructions end-to-end by interacting with real-world websites. We show that WebVoyager achieves a 59.1% task success rate on our benchmark, significantly surpassing the performance of both GPT-4 (All Tools) and the WebVoyager (text-only) setups.
  arXiv  Detail & Related papers  (2024-01-25T03:33:18Z)
• WebArena: A Realistic Web Environment for Building Autonomous Agents [92.3291458543633]
  We build an environment for language-guided agents that is highly realistic and reproducible. We focus on agents that perform tasks on the web, and create an environment with fully functional websites from four common domains. We release a set of benchmark tasks focusing on evaluating the functional correctness of task completions.
  arXiv  Detail & Related papers  (2023-07-25T22:59:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.