OpenAI Released Deep Research System Card
3 minutes
Deep Research is OpenAI's new agentic AI capability designed for conducting multi-step research across the internet. It can autonomously search, interpret, and analyze vast amounts of text, images, and PDFs to complete complex research tasks. The model is built on an early version of OpenAI o3, optimized for web browsing and data analysis.
Deep Research is designed to be adaptive, meaning it can pivot its search strategies as it encounters new information. It can also analyze user-provided files and run Python code for data processing and visualization.
Key Features
Deep Research goes beyond basic summarization and single-step querying. Instead of just retrieving information, it searches, evaluates, and synthesizes data, making it a much more capable research assistant.
- Autonomous web research: Searches, reads, and synthesizes information from multiple sources.
- Interprets diverse formats: Handles PDFs, images, and structured data.
- Python-based analysis: Executes calculations, creates visualizations, and processes datasets.
- Dynamic reasoning: Adjusts research direction based on new findings.
Training and Technical Foundations
Deep Research's capabilities are rooted in specialized training tailored for web-based research. The model was trained using new browsing datasets created specifically for research use cases. This process involved:
- Learning core browsing functions: Training included essential web tasks such as searching, clicking, scrolling, and file interpretation.
- Python tool integration: The model learned to operate a Python tool in a sandboxed setting, enabling it to perform calculations, data analysis, and graph plotting.
- Multi-task reinforcement learning: Through reinforcement learning, Deep Research was trained on a diverse range of tasks, from objective, auto-gradable tasks with ground truth answers to open-ended assignments guided by detailed rubrics.
- Safety-centric datasets: Alongside standard training data, the model was exposed to safety datasets from previous OpenAI o1 training, plus new browsing-specific safety datasets designed to tackle unique challenges in an internet research context.
Applications and Use Cases
Deep Research is poised to transform the landscape of complex research tasks. Its agentic capabilities make it highly versatile, suitable for:
- Academic and scientific research: Quickly aggregate and synthesize information from a multitude of sources to support in-depth studies.
- Business intelligence: Analyze market trends, competitive landscapes, and industry reports by parsing vast datasets online.
- Data journalism: Assist journalists in uncovering comprehensive narratives by connecting dots across diverse data points.
- Technical analysis: Read and interpret technical documents, code repositories, and white papers to provide detailed insights.
The flexibility of Deep Research means that users across various fields can benefit from its ability to autonomously explore and analyze large volumes of information.
How to Use Deep Research
Example 1: Conducting Market Research
Prompt
Deep Research Process:
- Searches authoritative sources and industry reports.
- Extracts and compares key trends across sources.
- Generates a structured market analysis report.
Example 2: Analyzing Financial Data
Prompt
Deep Research Process:
- Searches for financial reports and venture funding data.
- Extracts key figures and executes Python-based analysis.
- Outputs a growth trend visualization.
Final Thoughts
OpenAI's Deep Research marks a bold step forward in agentic AI capabilities. By combining robust web browsing, real-time data interpretation, and Python execution, the system offers depth in conducting complex research.
Valeriia Kuka
Valeriia Kuka, Head of Content at Learn Prompting, is passionate about making AI and ML accessible. Valeriia previously grew a 60K+ follower AI-focused social media account, earning reposts from Stanford NLP, Amazon Research, Hugging Face, and AI researchers. She has also worked with AI/ML newsletters and global communities with 100K+ members and authored clear and concise explainers and historical articles.