GTA: Generating Long-Horizon Tasks for Web Agents at Scale

• URL: http://arxiv.org/abs/2605.29218v1
• Date: Thu, 28 May 2026 01:05:50 GMT
• Title: GTA: Generating Long-Horizon Tasks for Web Agents at Scale
• Abstract summary: 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.
• Score: 82.43869456830664
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Web agents, which couple language models with browsing and tool-use capabilities, show promise as open web assistants. Yet progress is increasingly limited by the lack of scalable, process-level supervision. Existing benchmarks are largely manually constructed, providing only coarse start-goal annotations without intermediate trajectories, while recent automatic generation efforts remain expensive, biased, and shallow. These limitations prevent reliable training and evaluation of agents that must generalize to realistic, multi-hop, cross-page tasks. We introduce a scalable framework, GTA, that integrates crawling, retrieval-based seeding, in-context generation, and automated quality control to produce realistic tasks paired with executable trajectories. This design decouples crawling from generation for greater efficiency, grounds tasks in the site graph to enforce compositionality, and ensures dense supervision through deterministic replays and systematic validation. We instantiate the pipeline on over 50 websites covering e-commerce, government, forums, and news, with multilingual and multi-hop coverage. The resulting benchmark reveals a significant human-agent performance gap and enables detailed diagnostics. 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.

Related papers

• GTA-2: Benchmarking General Tool Agents from Atomic Tool-Use to Open-Ended Workflows [90.35728421223673]
  GTA-2 is a hierarchical benchmark for General Tool Agents (GTA)<n>Built on real-world authenticity, it leverages real user queries, deployed tools, and multimodal contexts.<n>Experiments reveal a pronounced capability cliff: while frontier models already struggle on atomic tasks, top models achieving only 14.39% success.
  arXiv  Detail & Related papers  (2026-04-17T05:36:00Z)
• SAGE: A Service Agent Graph-guided Evaluation Benchmark [27.342044311161654]
  We propose SAGE (Service Agent Graph-guided Evaluation), a universal benchmark for automated, dual-axis assessment.<n>SAGE formalizes unstructured SOPs into Dynamic Dialogue Graphs, enabling precise verification of logical compliance.<n>We also observe Empathy Resilience'', a phenomenon where models maintain polite conversational facades despite underlying logical failures.
  arXiv  Detail & Related papers  (2026-04-10T12:55:23Z)
• CarePilot: A Multi-Agent Framework for Long-Horizon Computer Task Automation in Healthcare [37.42599407869901]
  Multimodal agentic pipelines are transforming human-computer interaction by enabling efficient and accessible automation of complex, real-world tasks.<n>Recent efforts have focused on short-horizon or general-purpose applications, leaving long-horizon automation for domain-specific systems, particularly in healthcare, largely unexplored.<n>We introduce CarePilot, a multi-agent framework based on the actor-critic paradigm.<n>Our experiments show that CarePilot achieves state-of-the-art performance, outperforming strong closed-source and open-source multimodal baselines by approximately 15.26% and 3.38%, respectively.
  arXiv  Detail & Related papers  (2026-03-25T10:25:48Z)
• Scaling Web Agent Training through Automatic Data Generation and Fine-grained Evaluation [54.945281159783896]
  We present a scalable pipeline for automatically generating high-quality training data for web agents.<n>We introduce a novel constraint-based evaluation framework that provides fine-grained assessment of progress towards task completion.
  arXiv  Detail & Related papers  (2026-02-13T02:52:18Z)
• Graph2Eval: Automatic Multimodal Task Generation for Agents via Knowledge Graphs [38.93261732451012]
  We propose Graph2Eval, a knowledge graph-based framework that automatically generates both multimodal document comprehension tasks and web interaction tasks.<n>We instantiate the framework with Graph2Eval-Bench, a curated dataset of 1,319 tasks spanning document comprehension and web interaction scenarios.<n>Experiments show that Graph2Eval efficiently generates tasks that differentiate agent and model performance, revealing gaps in reasoning, collaboration, and web interaction across different settings.
  arXiv  Detail & Related papers  (2025-10-01T04:37:54Z)
• What Limits Virtual Agent Application? OmniBench: A Scalable Multi-Dimensional Benchmark for Essential Virtual Agent Capabilities [56.646832992178105]
  We introduce OmniBench, a cross-platform, graph-based benchmark with an automated pipeline for synthesizing tasks of controllable complexity.<n>We present OmniEval, a multidimensional evaluation framework that includes subtask-level evaluation, graph-based metrics, and comprehensive tests across 10 capabilities.<n>Our dataset contains 36k graph-structured tasks across 20 scenarios, achieving a 91% human acceptance rate.
  arXiv  Detail & Related papers  (2025-06-10T15:59:38Z)
• AgentTrek: Agent Trajectory Synthesis via Guiding Replay with Web Tutorials [53.376263056033046]
  Existing approaches rely on expensive human annotation, making them unsustainable at scale.<n>We propose AgentTrek, a scalable data synthesis pipeline that generates web agent trajectories by leveraging publicly available tutorials.<n>Our fully automated approach significantly reduces data collection costs, achieving a cost of just $0.55 per high-quality trajectory without human annotators.
  arXiv  Detail & Related papers  (2024-12-12T18:59:27Z)
• A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis [69.15016747150868]
  We introduce WebAgent, an agent that learns from self-experience to complete tasks on real websites. WebAgent plans ahead by decomposing instructions into canonical sub-instructions, summarizes long HTML documents into task-relevant snippets, and acts on websites. We empirically demonstrate that our modular recipe improves the success on real websites by over 50%, and that HTML-T5 is the best model to solve various HTML understanding tasks.
  arXiv  Detail & Related papers  (2023-07-24T14:56:30Z)

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.