RiskWebWorld: A Realistic Interactive Benchmark for GUI Agents in E-commerce Risk Management
RiskWebWorld offers 1,513 tasks showcasing challenges for GUI agents in e-commerce risk management, with top models achieving 49.1% success.
Key Findings
Methodology
RiskWebWorld evaluates GUI agents in e-commerce risk management using a Gymnasium-compliant infrastructure. This method decouples policy planning from environment mechanics, supporting scalable evaluation and agentic reinforcement learning. Tasks span 8 core domains, reflecting real challenges on uncooperative websites.
Key Results
- Top-tier generalist models achieve 49.1% success in RiskWebWorld, while specialized GUI models nearly fail completely, highlighting the importance of foundation model scale.
- Agentic reinforcement learning improves open-source models by 16.2%, demonstrating the infrastructure's effectiveness.
- Experiments reveal significant capability gaps in open-weight models for long-horizon tasks.
Significance
RiskWebWorld provides a practical testbed for developing robust digital workers, filling the gap in existing benchmarks for high-risk e-commerce environments. It demonstrates the importance of foundation models in long-horizon professional tasks, advancing research in academia and industry.
Technical Contribution
RiskWebWorld offers a scalable evaluation platform by decoupling policy planning and environment mechanics. It reveals the impact of foundation model scale on complex task performance and enhances open-source models through RL, offering new engineering possibilities.
Novelty
RiskWebWorld is the first highly realistic interactive benchmark for e-commerce risk management, filling the gap in evaluating GUI agents in complex commercial environments. Compared to existing work, it focuses more on dynamic risk analysis in real environments.
Limitations
- Specialized GUI models perform poorly in long-horizon tasks due to action misrouting and argument hallucination.
- The current benchmark does not fully simulate all possible environmental hijackments.
- Further research is needed to improve model performance in multi-page evidence composition.
Future Work
Future work will focus on enhancing model performance in complex tasks, exploring more effective policy planning methods, and expanding the benchmark to cover more commercial domains and environmental hijackments.
AI Executive Summary
RiskWebWorld is the first highly realistic interactive benchmark designed for e-commerce risk management. Existing GUI agent benchmarks primarily target predictable consumer environments, while RiskWebWorld covers 1,513 tasks, showcasing real challenges in risk operations on uncooperative websites.
The benchmark uses a Gymnasium-compliant infrastructure to decouple policy planning from environment mechanics, supporting scalable evaluation and agentic reinforcement learning. Experiments show that top-tier generalist models achieve 49.1% success in RiskWebWorld, while specialized GUI models nearly fail completely, highlighting the importance of foundation model scale.
RiskWebWorld provides a practical testbed for developing robust digital workers, advancing research in academia and industry. Future work will focus on enhancing model performance in complex tasks and exploring more effective policy planning methods.
Deep Analysis
Background
As GUI agents evolve, they demonstrate strong capabilities in automating interactions with software environments. However, existing interactive benchmarks focus on predictable consumer tasks, lacking evaluations in high-risk commercial environments. RiskWebWorld addresses this gap.
Core Problem
In e-commerce risk management, GUI agents must perform complex dynamic risk analyses on uncooperative websites. These tasks require long-horizon planning and adaptability, which current benchmarks fail to adequately reflect.
Innovation
RiskWebWorld's innovation lies in its highly realistic task design and Gymnasium-compliant infrastructure. It decouples policy planning from environment mechanics, supporting scalable evaluation and agentic reinforcement learning, providing a scalable evaluation platform.
Methodology
- �� Task Design: Covers 1,513 tasks across 8 core domains.
- �� Infrastructure: Gymnasium-compliant, decouples policy planning from environment mechanics.
- �� Evaluation Method: Supports scalable evaluation and agentic reinforcement learning.
- �� Data Collection: Sourced from production risk-control pipelines.
Experiments
Experiments used various models, including generalist and specialized GUI models. By comparing different models' performance in RiskWebWorld, the impact of foundation model scale on complex task performance is revealed. Agentic reinforcement learning further enhances open-source models.
Results
Top-tier generalist models achieve 49.1% success in RiskWebWorld, while specialized GUI models nearly fail completely. Agentic reinforcement learning improves open-source models by 16.2%, demonstrating the infrastructure's effectiveness.
Applications
RiskWebWorld can be used to evaluate and develop robust digital workers, particularly in e-commerce environments requiring complex dynamic risk analysis. It provides a practical testbed for academia and industry.
Limitations & Outlook
Specialized GUI models perform poorly in long-horizon tasks due to action misrouting and argument hallucination. The current benchmark does not fully simulate all possible environmental hijackments, requiring further research to improve model performance in multi-page evidence composition.
Plain Language Accessible to non-experts
Imagine shopping in a complex market with many different stores, each with its own rules and obstacles. RiskWebWorld is like a virtual shopping assistant that helps you navigate these stores, finding hidden risks and opportunities. It needs to react quickly and handle sudden obstacles, like checkpoints requiring identity verification or sudden pop-up ads. In this way, RiskWebWorld tests these virtual assistants' abilities in complex environments.
ELI14 Explained like you're 14
Imagine playing a complex game with many levels, each with different challenges and obstacles. RiskWebWorld is like a super-smart game assistant that helps you find the best route, avoid traps, and complete tasks in these levels. It needs to react quickly and handle sudden obstacles, like puzzles to unlock or sudden enemies. In this way, RiskWebWorld tests these assistants' abilities in complex games!
Glossary
GUI Agent
GUI agents are intelligent systems that can autonomously interact with software environments, typically by perceiving visual rendering states and underlying structural data to execute tasks.
Used in the paper to evaluate their performance in e-commerce risk management.
Gymnasium-compliant
Refers to RiskWebWorld's infrastructure being compatible with Gymnasium standards, supporting scalable evaluation and reinforcement learning.
Used to decouple policy planning from environment mechanics.
Environmental Hijackments
Refers to various obstacles encountered during risk operations on uncooperative websites, such as verification barriers and dynamic content changes.
Used in task design to test agents' adaptability.
Agentic Reinforcement Learning
A method to enhance agent capabilities through interactive training, especially in complex tasks.
Used to improve open-source models' performance in RiskWebWorld.
Foundation Model
Refers to large language or vision-language models as core components of GUI agents.
Used in experiments to evaluate their performance in complex tasks.
Open Questions Unanswered questions from this research
- 1 How to improve specialized GUI models' performance in long-horizon tasks? Current methods have shortcomings in action misrouting and argument hallucination.
- 2 How to better simulate all possible environmental hijackments? The current benchmark does not fully cover them.
Applications
Immediate Applications
E-commerce Risk Management
RiskWebWorld can be used to evaluate and develop digital workers for complex dynamic risk analysis in e-commerce environments.
Long-term Vision
Intelligent Digital Assistants
By enhancing GUI agents' capabilities, RiskWebWorld may drive the application of intelligent digital assistants in more commercial domains.
Abstract
Graphical User Interface (GUI) agents show strong capabilities for automating web tasks, but existing interactive benchmarks primarily target benign, predictable consumer environments. Their effectiveness in high-stakes, investigative domains such as authentic e-commerce risk management remains underexplored. To bridge this gap, we present RiskWebWorld, the first highly realistic interactive benchmark for evaluating GUI agents in e-commerce risk management. RiskWebWorld features 1,513 tasks sourced from production risk-control pipelines across 8 core domains, and captures the authentic challenges of risk operations on uncooperative websites, partially environmental hijackments. To support scalable evaluation and agentic reinforcement learning (RL), we further build a Gymnasium-compliant infrastructure that decouples policy planning from environment mechanics. Our evaluation across diverse models reveals a dramatic capability gap: top-tier generalist models achieve 49.1% success, while specialized open-weights GUI models lag at near-total failure. This highlights that foundation model scale currently matters more than zero-shot interface grounding in long-horizon professional tasks. We also demonstrate the viability of our infrastructure through agentic RL, which improves open-source models by 16.2%. These results position RiskWebWorld as a practical testbed for developing robust digital workers.