2025年，AI 的演进正从"能力突破"迈向"系统构建"阶段。 自主智能体开始尝试真实任务闭环，世界模型在复杂环境中持续验证，推理架构与训练范式不断重构——技术的焦点，已不再只是"能不能做"，而是"如何做得更 可靠、更可解释、更可持续"。 在这一背景下，NeurIPS 作为全球人工智能与机器学习领域最具影响力的学术会议之一，再次成为观察前沿风向的关键窗口。今年大会共收到 21575 份有效投 稿，最终接收 5290 篇，整体录用率为 24.52%。大会将于 2025 年 12 月 2 日到 7 日在美国圣地亚哥举办，并且首次设置了第二个官方分会场墨西哥城，标志着全 球 AI 学术生态的多元化布局正在加速成型。 为了服务中国 AI 社区，近年来机器之心持续举办了多场 NeurIPS、CVPR、ACL、ICLR 论文分享会，受到了海内外 AI 社区的极大关注，众多高校、企业都积极 参与。 作为一场为国内 AI 人才打造的盛会，本次「NeurIPS 2025 论文分享会」设置了 Keynote、论文分享、圆桌对话、Poster 交流及企业展位交流等丰富环节。今天， 论文分享会的全日程、Keynote 分享嘉宾及演讲 ...

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].