Core Insights - The evolution of AI is transitioning from "capability breakthroughs" to "system construction" by 2025, focusing on reliability, interpretability, and sustainability [2] - NeurIPS 2025 will be held from December 2 to 7 in San Diego, USA, with a record of 21,575 submissions and an acceptance rate of 24.52%, indicating a growing global AI academic ecosystem [2] - The event aims to serve the Chinese AI community through various activities, including keynote speeches, paper sharing, roundtable discussions, and poster sessions [3] Event Details - The "NeurIPS 2025 Paper Sharing Conference" will take place on November 22, 2025, from 09:00 to 17:30 at the Crowne Plaza Hotel in Zhongguancun, Beijing [5][6] - The agenda includes keynote speeches, paper presentations, and poster exchanges, providing a platform for academic and industry collaboration [3][10] Keynote Speakers - The morning keynote will be delivered by Professor Qiu Xipeng from Fudan University, focusing on "Contextual Intelligence: Completing the Key Puzzle of AGI" [14][16] - The afternoon keynote speaker is Fan Qi from Nanjing University, with the topic yet to be determined [17] Paper Presentations - A variety of papers will be presented, covering topics such as data mixing, multimodal adaptation, and reinforcement learning in large language models [9][11][23] - Notable presentations include "Data Mixing Can Induce Phase Transitions in Knowledge Acquisition" and "Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model" [9][11]

Core Viewpoint - The article discusses the concept of Context Scaling as a crucial step towards achieving Artificial General Intelligence (AGI), emphasizing the need for AI to understand and adapt to complex and ambiguous contexts rather than merely increasing model size or data volume [2][21]. Summary by Sections Evolution of Large Models - The evolution of large models is summarized in three acts: 1. The first act focuses on the success of model scaling, where data and parameters are stacked to compress knowledge, leading to the emergence of models like ChatGPT and MOSS [6]. 2. The second act involves post-training optimization, enhancing decision-making capabilities through methods like reinforcement learning and multi-modal approaches, exemplified by models such as GPT o1/o3 and DeepSeek-R1 [6][7]. 3. The third act, Context Scaling, aims to address the challenges of defining context to improve model capabilities, particularly in complex and nuanced situations [8][21]. Context Scaling - Context Scaling is defined as the ability of AI to understand and adapt to rich, complex, and dynamic contextual information, which is essential for making reasonable judgments in ambiguous scenarios [8][9]. - The concept of "tacit knowledge" is introduced, referring to the implicit understanding that humans possess but is difficult to articulate, which AI must learn to capture [11][12]. Three Technical Pillars - Context Scaling is supported by three key capabilities: 1. Strong Interactivity: AI must learn from interactions, understanding social cues and cultural nuances [14][15]. 2. Embodiment: AI needs a sense of agency to perceive and act within its environment, which can be tested in virtual settings [16]. 3. Anthropomorphizing: AI should resonate emotionally with humans, understanding complex social interactions and cultural sensitivities [17]. Challenges and Integration - The article highlights that Context Scaling is not a replacement for existing scaling methods but rather complements them by focusing on the quality and structure of input data [18]. - It also redefines the environment for reinforcement learning, moving beyond simple state-action-reward loops to include rich contextual information [20]. Conclusion - The exploration of Context Scaling aims to unify various technological paths under the core goal of contextual understanding, which is seen as essential for navigating the complexities of the real world and a potential key to achieving AGI [22].