英伟达推出通用深度研究系统，可接入任何LLM，支持个人定制

Core Viewpoint - NVIDIA has introduced a Universal Deep Research (UDR) system that supports personalized customization and can interface with any large language model (LLM) [1][2]. Summary by Sections General Overview - The UDR system allows users to fully customize deep research strategies and delegate tasks to intelligent agents [2][10]. - A user interface prototype for UDR is available for download on GitHub, showcasing its versatility [3]. Features and Innovations - UDR enables users to create, edit, and optimize their customized deep research strategies without the need for additional training or fine-tuning [6]. - The system can compile strategies from natural language into executable research orchestration code, delivering final reports to users [11]. - Key innovative features include: - Customizable research strategies defined in natural language, which the system converts into executable code [12]. - A decoupled architecture that allows any LLM to be integrated into a complete deep research tool [13]. - Enhanced product design flexibility, enabling the use of advanced AI models alongside tailored research solutions [14]. User Interface and Control - The prototype showcases four practical functions: real-time strategy modification, preset strategy library selection, progress notifications, and report viewing [15]. - The interface includes a code agent for coordinating LLMs and tools, but lacks user control over resource prioritization and information verification [16]. Efficiency and Cost Management - UDR improves computational efficiency by separating control logic from LLM reasoning, with the entire research process managed by generated code running on the CPU [19]. - The system only calls the LLM when user-defined strategies require it, significantly reducing GPU resource consumption and overall execution costs [20]. Limitations and Future Improvements - The accuracy of UDR's execution of research strategies depends on the quality of the underlying AI model's code generation [21]. - The system assumes user-designed strategies are reasonable and executable, performing only basic checks [21]. - Current limitations include a lack of user intervention during execution and the need for all decisions to be pre-set, which reduces flexibility for long-term or exploratory research tasks [22]. - Proposed improvements include customizable strategy libraries and enhanced user control over LLM reasoning processes [23]. Current Status - The UDR system is still in the prototype phase and has not been officially launched, but there are expectations for a fully functional version in the future [25].