# Evaluation Sheet for Deep Research: A Use Case for Academic Survey Writing

arXiv: https://arxiv.org/abs/2510.01283 · HTML: https://mlsanity.com/paper/2510.01283/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 11 references that are in the map.
- First posted on arXiv 30 Sep 2025.

## Builds on (most cited first, 4 of 4)

- [When Search Engine Services meet Large Language Models: Visions and Challenges](https://mlsanity.com/paper/2407.00128/) (2024)
- [Agentic Systems: A Guide to Transforming Industries with Vertical AI Agents](https://mlsanity.com/paper/2501.00881/) (2025)
- [Natural Language Processing in Ethiopian Languages: Current State, Challenges, and Opportunities](https://mlsanity.com/paper/2303.14406/) (2023)
- [State of NLP in Kenya: A Survey](https://mlsanity.com/paper/2410.09948/) (2024)

## Abstract (arXiv)

Large Language Models (LLMs) powered with argentic capabilities are able to do knowledge-intensive tasks without human involvement. A prime example of this tool is Deep research with the capability to browse the web, extract information and generate multi-page reports. In this work, we introduce an evaluation sheet that can be used for assessing the capability of Deep Research tools. In addition, we selected academic survey writing as a use case task and evaluated output reports based on the evaluation sheet we introduced. Our findings show the need to have carefully crafted evaluation standards. The evaluation done on OpenAI`s Deep Search and Google's Deep Search in generating an academic survey showed the huge gap between search engines and standalone Deep Research tools, the shortcoming in representing the targeted area.