Deep Research Comparator: A Platform For Fine-grained Human Annotations of Deep Research Agents
Prahaladh Chandrahasan, Jiahe Jin, Zhihan Zhang, Tevin Wang, Andy Tang, Lucy Mo, Morteza Ziyadi, Leonardo F.R. Ribeiro, Zimeng Qiu, Markus Dreyer, Akari Asai, Chenyan Xiong

TL;DR
Deep Research Comparator is a platform that enables detailed evaluation and comparison of deep research agents, including their intermediate steps, to improve assessment and development of such AI systems.
Contribution
The paper introduces a comprehensive platform for evaluating deep research agents with fine-grained human feedback and a baseline agent scaffold for easy integration of large language models.
Findings
Collected real user preference data from 17 annotators.
Demonstrated the platform's utility through a practical evaluation.
Provided a baseline agent scaffold for research and development.
Abstract
Effectively evaluating deep research agents that autonomously search the web, analyze information, and generate reports remains a major challenge, particularly when it comes to assessing long reports and giving detailed feedback on their intermediate steps. To address these gaps, we introduce Deep Research Comparator, a platform that offers a holistic framework for deep research agent hosting, side-by-side comparison, fine-grained human feedback collection, and ranking calculation. Given a user query, our platform displays the final reports from two different agents along with their intermediate steps during generation. Annotators can evaluate the overall quality of final reports based on side-by-side comparison, and also provide detailed feedback separately by assessing intermediate steps or specific text spans within the final report. Furthermore, we develop Simple Deepresearch, an…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMobile Crowdsensing and Crowdsourcing · Topic Modeling · Expert finding and Q&A systems
