Research Control
Research Control refers to the granular control capabilities provided to users within the ChatGPT Deep Research feature. This functionality allows users to specify and manage which sources are utilized during the research process, enabling more customized and targeted information gathering according to individual needs and preferences.
Source Management
Through Research Control, users can influence the selection and prioritization of sources that inform the research output. This capability helps users tailor results to align with their specific requirements, quality standards, and subject matter expertise. Users may specify preferences regarding source types, domains, or particular publications to be included or excluded from the research process.
Application and Purpose
The feature addresses the need for transparency and user agency in AI-driven research workflows. By allowing users to set parameters around source selection, Research Control helps ensure that generated research outputs reflect appropriate evidence and information sources relevant to the user’s context, whether for academic, professional, or informational purposes.
Source Notes
- 2026-04-07: Analysis of Leading AI Models Capabilities Pricing Tiers and Optimal · ▶ source
- 2026-04-08: LiteParse Free Local Layout Preserving Document Parsing for LLMs · ▶ source
- 2026-04-10: Google NotebookLMs Latest Features Enhanced Infographics AI Videos · ▶ source
- 2026-04-12: DreamDojo AI Bridging Robotics Sim2Real Gap for Complex Tasks · ▶ source
- 2026-04-13: Tesla FSD Supervised European Approval Netherlands Sets Precedent for · ▶ source
- 2026-04-15: Hermes Agent Self Improving AI for Adaptive User Learning · ▶ source
- 2026-04-21: Google DeepMind
- 2026-04-26: Fungal Ice-Nucleating Proteins: A Groundbreaking Rain Discovery · ▶ source