你是否也曾榨干过DeepSeek？

Core Insights - The article discusses the performance of AI models, particularly in the context of OpenAI's BrowseComp test, which evaluates the ability of AI agents to locate complex and entangled information [10][11][12]. Group 1: AI Model Performance - AI models can generate answers quickly, often within a minute, but struggle with certain types of questions that require deeper reasoning and extensive information retrieval [1][9]. - The BrowseComp test features questions that are simple in answer but complex in their descriptions, making it challenging for models to identify the correct information [14][15]. - The performance of various models in the BrowseComp test shows that even the best-performing models achieve only around 50% accuracy, indicating significant room for improvement [25][29]. Group 2: Testing Methodology - The BrowseComp test consists of 1266 questions, and the complexity arises from the vague and misleading characteristics of the questions, which require extensive searching across multiple sources [27][28]. - The results indicate that models like GPT-4o and OpenAI's o1 have low accuracy rates, with the highest being 9.9% for o1 when not connected to the internet [29]. Group 3: Implications for Future Development - Despite current limitations, AI models are rapidly improving in their browsing and information retrieval capabilities, suggesting a positive trend for future developments [31]. - Engaging with AI models multiple times and refining questions can enhance the quality of responses, indicating a need for iterative interaction to maximize the utility of these models [33].