Generative AI Act II: Test Time Scaling Drives Cognition Engineering
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
680634b8e4e8571bc15e09af
Shijie Xia
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2025-04-22T09:52:15.724Z
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shijie xia
user
seven-cat
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Yiwei Qin
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Xuefeng Li
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Yan Ma
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2025-05-26T08:40:58.372Z
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Yan Ma
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ManTle
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Run-Ze Fan
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2025-04-22T09:52:13.044Z
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Run-Ze Fan
user
Vfrz
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Steffi Chern
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Haoyang Zou
680634b8e4e8571bc15e09b6
Fan Zhou
680634b8e4e8571bc15e09b7
Xiangkun Hu
680634b8e4e8571bc15e09b8
Jiahe Jin
680634b8e4e8571bc15e09b9
Yanheng He
claimed_verified
2025-05-20T07:23:07.007Z
661b9ac57cfb7bcb3057a578
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Yanheng He
user
henryhe0123
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Yixin Ye
680634b8e4e8571bc15e09bb
Yixiu Liu
680634b8e4e8571bc15e09bc
Pengfei Liu
claimed_verified
2025-06-29T10:14:34.465Z
6144a0c4ff1146bbd84d9865
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Pengfei Liu
user
Pengfei
Generative AI Act II: Test Time Scaling Drives Cognition Engineering
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
Transition from knowledge-retrieval to thought-construction in generative AI through test-time scaling techniques marks a new era of human-AI interaction.
Large Language Models prompt engineering knowledge-retrieval systems latent space thought-construction engines test-time scaling cognition engineering