DeepSearchQA - Google's Open Source AI Research Agent Testing Benchmarks

What is DeepSearchQA

DeepSearchQA is Google's open source AI research Agent test benchmark, specifically designed to evaluate the performance of intelligences in complex multi-step query tasks. It consists of 900 hand-designed "causal chain" tasks covering 17 domains, requiring AI to generate complete answers through multi-step reasoning just like human researchers. The benchmarks emphasize comprehensiveness rather than pure accuracy, and measure the AI's memory capacity and thinking efficiency. Currently, DeepSearchQA has been applied to the performance evaluation of Gemini Deep Research Agent, and the latest version of the benchmark scored 46.4%, which is better than GPT-5 Pro. Developers can get the open-source code to participate in the competition through the Kaggle platform.

DeepSearchQA's functionality features

• Complex and cross-cutting mission design The task consists of 900 hand-designed "causal chain" tasks covering 17 domains, each step of which relies on antecedent analysis and requires the intelligences to generate an exhaustive set of answers, thus measuring their research accuracy and search comprehensiveness.
• Comprehensive assessment : Unlike traditional fact-based tests, DeepSearchQA focuses more on evaluating the comprehensiveness of an intelligent in a multi-step complex retrieval task, and is able to test the retrieval memory capacity of an intelligent.
• Diagnostic tool as a benefit of "thinking time" : Google's internal tests show that the performance of an intelligence is significantly improved when it is allowed to perform more search and reasoning steps, and DeepSearchQA can be used as a tool to measure the benefits of "thinking time".
• open source : The dataset and tools are open source, and developers can access the dataset, leaderboards and Colab examples, and read the dataset technical report.

DeepSearchQA's Core Benefits

• Complex and cross-cutting mandates : Contains 900 hand-designed "causal chain" tasks across 17 domains, each step relying on antecedent analysis to comprehensively assess the performance of intelligences in complex multi-step research tasks.
• Measuring comprehensiveness : Unlike traditional fact-based tests, DeepSearchQA requires intelligences to generate exhaustive answer sets that not only assess the accuracy of the research, but also measure the memorization ability of the retrieval, which is more relevant to real-world research needs.
• Diagnosable "thinking hours" benefit : Google's internal evaluation found that when intelligences are allowed to perform more search and reasoning steps, their performance improves significantly, and DeepSearchQA can be used as a tool to measure the efficiency of "think time".

What is DeepSearchQA's official website?

• Project website:: https://blog.google/technology/developers/deep-research-agent-gemini-api/
• open source address:: https://www.kaggle.com/benchmarks/google/dsqa/leaderboard
• Technical Papers:: https://storage.googleapis.com/deepmind-media/DeepSearchQA/DeepSearchQA_benchmark_paper.pdf

Who is DeepSearchQA for?

• Machine Learning Engineer : Optimize the model with the help of this benchmark test to improve the comprehensiveness and accuracy of intelligences in multi-step complex retrieval tasks and develop more efficient research tools.
• natural language processing (NLP) expert : To further improve the performance of natural language processing models by testing the intelligences' ability to understand and execute natural language instructions through DeepSearchQA.
• data scientist : Data analysis and model training using DeepSearchQA's datasets and tools to explore the potential of intelligences for applications in different domains.
• Developers in related fields : You can use DeepSearchQA's open source resources and tools to develop and optimize intelligences for a variety of scenarios that require complex information retrieval and analysis.