AI Frontier Insights | Next-Generation Large-Scale Models: Is "Long-Term Thinking" Still the Key? 🤔 Over the past two years, OpenAI o1 and DeepSeek-R1 have ignited intense competition in inference, with companies frantically increasing inference computing power and extending the thinking chain. But have you ever considered: Why do simply forcibly merging "thinking mode + instruction mode" often yield mediocre results and underperform on both ends? Can long-term thinking that only involves static, closed-door deduction truly solve complex real-world tasks? When large-scale models are no longer competing on problem-solving and benchmark scores, what capabilities will truly be the core competitive advantage of the next generation? The industry's answer is shifting towards 👉 Agentic Intelligent Action Thinking Upgrading from "daydreaming" to "thinking while using tools + interactive environment + dynamically modifying solutions + autonomous implementation." But new challenges arise: How to build a high-fidelity training environment? How to avoid reward cheating and false optimization? In the future competition, will model algorithms prevail, or will environmental engineering and closed-loop capabilities determine the outcome? From training large models → training intelligent agents → training intelligent systems, who will be the first to successfully establish the entire ecosystem? A question worth pondering deeply by every AI practitioner 🔍 twitter.com/LXDAO_Official/sta...