BEARCUBS: A benchmark for computer-using web agents
2025-07-18
Summary
The article introduces BEARCUBS, a benchmark designed to evaluate the capabilities of modern web agents in real-world scenarios by using live web content rather than simulated environments. BEARCUBS comprises 111 questions that require agents to demonstrate a range of skills, including text and multimodal interactions, to find factual information. Human participants outperformed the state-of-the-art agents, with humans achieving 84.7% accuracy while the best-performing agent only reached 23.4%.
Why This Matters
The development and evaluation of web agents are crucial as these technologies have the potential to significantly assist users in navigating complex online tasks. However, current benchmarks often do not capture the complexity and unpredictability of real-world web interactions. BEARCUBS addresses this gap, providing a more realistic and challenging test that highlights areas where web agents need improvement, particularly in multimodal interactions and reliable source selection.
How You Can Use This Info
Professionals interested in AI and web technologies can use BEARCUBS to understand the current limitations and potential of web agents, which can inform decision-making related to AI adoption and integration. Organizations developing web agents can leverage insights from BEARCUBS to focus on enhancing multimodal capabilities and ensuring their agents can interact effectively in real-world scenarios. This understanding can also guide investment in AI technologies that offer the most practical benefits for complex online tasks.