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].

Core Viewpoint - The emergence of "Software 3.5" signifies a paradigm shift in human-AI interaction, moving from traditional input-output models to cognitive collaboration, where AI acts as a transparent thinking partner rather than a mere tool [1][8][24]. Group 1: Evolution of Software Paradigms - Software 3.0 is considered outdated as it was based on the limitations of earlier AI capabilities, primarily focused on text generation and simple reasoning [6][20]. - The transition to Software 3.5 reflects a generational leap in AI capabilities, enabling true cognitive collaboration where AI understands not just commands but the underlying motivations and context [6][25]. - The new paradigm emphasizes that intelligence emerges from the interaction between humans and AI, rather than being a solitary attribute of either [7][37]. Group 2: Characteristics of Software 3.5 - Software 3.5 introduces a cognitive collaboration model, allowing for real-time interaction and adjustments, where users can intervene at any point in the AI's thought process [24][26]. - This model supports asynchronous collaboration, enabling AI to continue processing and exploring even when the user is offline, enhancing the overall efficiency of human-AI teamwork [26][27]. - The interface requirements for Software 3.5 necessitate a fundamental redesign to accommodate complex cognitive interactions, moving beyond simple Q&A formats [27][28]. Group 3: New Skills for Developers - Developers in the Software 3.5 era must acquire new skills, including cognitive modeling, intent engineering, and context management, to effectively design interactions that leverage AI's cognitive capabilities [28][30]. - Real-time interaction design and asynchronous collaboration architecture are essential skills for creating systems that allow for dynamic user engagement and cognitive transparency [30][31]. - The evolution from traditional programming to cognitive collaboration signifies that anyone can become a cognitive architect, emphasizing the democratization of software development [31][32].