Generative AI Act II: Test Time Scaling Drives Cognition Engineering
Authors: name: Shijie Xia, name: Yiwei Qin, name: Xuefeng Li, name: Yan Ma, name: Run-Ze Fan, name: Steffi Chern, name: Haoyang Zou, name: Fan Zhou, name: Xiangkun Hu, name: Jiahe Jin, name: Yanheng He, name: Yixin Ye, name: Yixiu Liu, name: Pengfei Liu

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