Video-BrowseComp: A Benchmark for Agentic Video Research
Analysis
This paper introduces Video-BrowseComp, a new benchmark designed to evaluate agentic video reasoning capabilities of AI models. It addresses a significant gap in the field by focusing on the dynamic nature of video content on the open web, moving beyond passive perception to proactive research. The benchmark's emphasis on temporal visual evidence and open-web retrieval makes it a challenging test for current models, highlighting their limitations in understanding and reasoning about video content, especially in metadata-sparse environments. The paper's contribution lies in providing a more realistic and demanding evaluation framework for AI agents.
Key Takeaways
- •Introduces Video-BrowseComp, a new benchmark for agentic video research on the open web.
- •Emphasizes the need for temporal visual evidence and open-web retrieval.
- •Highlights the limitations of current models in reasoning about video content, especially in metadata-sparse environments.
- •Provides a more realistic and demanding evaluation framework for AI agents.
“Even advanced search-augmented models like GPT-5.1 (w/ Search) achieve only 15.24% accuracy.”