用测试时扩展让AI学会深度思考，开启认知工程新阶段
Generative AI Act II: Test Time Scaling Drives Cognition Engineering
- 通过测试时扩展技术，让AI从查知识转向构造思维
- 实现语言级思想交互，突破传统提示工程局限
- 开源教程与代码，推动普通开发者参与认知工程
生成式AI的第一阶段（2020–2023）依赖大规模参数和数据训练，虽取得显著成就，但存在知识延迟、浅层推理和认知能力受限等根本问题。该阶段主要通过提示工程实现与AI的自然语言对话。如今进入第二阶段（2024年至今），模型正从隐空间的知识检索系统转变为基于测试时扩展的认知建构引擎。这一新范式通过语言化思维建立与AI的思维层面连接。本文阐明认知工程的概念基础，解释当前发展关键性，系统梳理相关方法并提供优化实现，降低门槛，使每位从业者都能参与生成式AI的第二幕。我们维护一个持续更新的测试时扩展论文集合，详见GitHub仓库：https://github.com/GAIR-NLP/cognition-engineering
原文摘要 · Abstract (English)
The first generation of Large Language Models - what might be called "Act I" of generative AI (2020-2023) - achieved remarkable success through massive parameter and data scaling, yet exhibited fundamental limitations such as knowledge latency, shallow reasoning, and constrained cognitive processes. During this era, prompt engineering emerged as our primary interface with AI, enabling dialogue-level communication through natural language. We now witness the emergence of "Act II" (2024-present), where models are transitioning from knowledge-retrieval systems (in latent space) to thought-construction engines through test-time scaling techniques. This new paradigm establishes a mind-level connection with AI through language-based thoughts. In this paper, we clarify the conceptual foundations of cognition engineering and explain why this moment is critical for its development. We systematically break down these advanced approaches through comprehensive tutorials and optimized implementations, democratizing access to cognition engineering and enabling every practitioner to participate in AI's second act. We provide a regularly updated collection of papers on test-time scaling in the GitHub Repository: https://github.com/GAIR-NLP/cognition-engineering
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