Generative AI Act II: Test Time Scaling Drives Cognition Engineering
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arXiv
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| Format: | Preprint |
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2025
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| author | Xia, Shijie Qin, Yiwei Li, Xuefeng Ma, Yan Fan, Run-Ze Chern, Steffi Zou, Haoyang Zhou, Fan Hu, Xiangkun Jin, Jiahe He, Yanheng Ye, Yixin Liu, Yixiu Liu, Pengfei |
| author_facet | Xia, Shijie Qin, Yiwei Li, Xuefeng Ma, Yan Fan, Run-Ze Chern, Steffi Zou, Haoyang Zhou, Fan Hu, Xiangkun Jin, Jiahe He, Yanheng Ye, Yixin Liu, Yixiu Liu, Pengfei |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_13828 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generative AI Act II: Test Time Scaling Drives Cognition Engineering Xia, Shijie Qin, Yiwei Li, Xuefeng Ma, Yan Fan, Run-Ze Chern, Steffi Zou, Haoyang Zhou, Fan Hu, Xiangkun Jin, Jiahe He, Yanheng Ye, Yixin Liu, Yixiu Liu, Pengfei Computation and Language Artificial Intelligence 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 |
| title | Generative AI Act II: Test Time Scaling Drives Cognition Engineering |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2504.13828 |