Core Viewpoint - The article introduces the concept of "Deep Cognition," a platform designed to enhance cognitive accumulation through interactive AI, transforming the way users engage with knowledge and insights [1][19][60]. Group 1: User Engagement and Cognitive Accumulation - Users often collect articles and papers but rarely revisit them, with statistics showing low revisit rates: an average of 547 articles collected on Zhihu with a 3.2% revisit rate, 284 papers for graduate students with a 12% deep reading rate, and 1,203 items on Xiaohongshu with a 1.8% secondary browsing rate [4][5]. - The platform aims to change this by allowing users to accumulate cognitive assets with each interaction, where every collection contributes to the AI's learning and understanding [7][11]. Group 2: Features of the Deep Cognition Platform - The platform offers features such as cognitive rankings and weekly summaries, showcasing popular cognitive topics and community learning dynamics [12]. - It includes personalized subscription and sharing options, allowing users to tailor their cognitive experience [15]. - The cognitive synthesis feature merges diverse viewpoints to create deeper understanding and insights [33]. Group 3: Technical Foundations and Innovations - The underlying technology is based on the principle of "Interaction as Intelligence," emphasizing the collaborative relationship between humans and AI [23][24]. - The platform's cognitive card generation engine transforms complex research outcomes into structured, visual insights, making them easier to understand [33]. - The cognitive accumulation mechanism uses user behavior data to drive personalized recommendations, ensuring that each learning experience builds on existing knowledge [33]. Group 4: Performance and User Experience - Experiments demonstrate that the introduction of interactive features significantly enhances the quality of reports generated by the system, with an average quality improvement of 63% compared to non-interactive versions [34][39]. - The system outperforms leading commercial deep research systems in user experience metrics, particularly in transparency and fine-grained interaction [36][42]. - The collaborative model shows that expert users achieve a 72.73% accuracy rate when interacting with the system, compared to much lower rates for non-expert users and autonomous AI systems [44][46]. Group 5: Future Implications - The platform signifies a shift from viewing AI as merely an efficiency tool to recognizing it as a cognitive partner, redefining human-AI collaboration [19][60]. - The findings suggest that effective human-AI collaboration requires a flexible control mechanism, allowing users to switch between hands-on and hands-off approaches based on task demands [50][57].