title: Generative AI Act II: Test Time Scaling Drives Cognition Engineering
summary: 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 in 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
paper_authors._id: 680634b8e4e8571bc15e09af
paper_authors.name: Shijie Xia
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paper_authors.fullname: shijie xia
paper_authors.type: user
paper_authors.user: seven-cat
paper_authors._id: 680634b8e4e8571bc15e09b0
paper_authors.name: Yiwei Qin
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paper_authors.name: Xuefeng Li
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paper_authors.name: Yan Ma
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paper_authors.fullname: Yan Ma
paper_authors.type: user
paper_authors.user: ManTle
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paper_authors.name: Run-Ze Fan
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paper_authors.fullname: Run-Ze Fan
paper_authors.type: user
paper_authors.user: Vfrz
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paper_authors.name: Steffi Chern
paper_authors._id: 680634b8e4e8571bc15e09b5
paper_authors.name: Haoyang Zou
paper_authors._id: 680634b8e4e8571bc15e09b6
paper_authors.name: Fan Zhou
paper_authors._id: 680634b8e4e8571bc15e09b7
paper_authors.name: Xiangkun Hu
paper_authors._id: 680634b8e4e8571bc15e09b8
paper_authors.name: Jiahe Jin
paper_authors._id: 680634b8e4e8571bc15e09b9
paper_authors.name: Yanheng He
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-20T07:23:07.007Z
paper_authors._id: 661b9ac57cfb7bcb3057a578
paper_authors.avatarUrl: /avatars/f8afaa8eaad3a1e5963a4feebec3f7ab.svg
paper_authors.fullname: Yanheng He
paper_authors.type: user
paper_authors.user: henryhe0123
paper_authors._id: 680634b8e4e8571bc15e09ba
paper_authors.name: Yixin Ye
paper_authors._id: 680634b8e4e8571bc15e09bb
paper_authors.name: Yixiu Liu
paper_authors._id: 680634b8e4e8571bc15e09bc
paper_authors.name: Pengfei Liu
paper_title: Generative AI Act II: Test Time Scaling Drives Cognition Engineering
paper_summary: 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 in 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
paper_ai_summary: Transition from knowledge-retrieval to thought-construction in generative AI through test-time scaling techniques marks a new era of human-AI interaction.
paper_ai_keywords: Large Language Models prompt engineering knowledge-retrieval systems latent space thought-construction engines test-time scaling cognition engineering