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The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
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Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
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From System 1 to System 2: A Survey of Reasoning Large Language Models.

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    Summary
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    This survey explores reasoning Large Language Models (LLMs) that bridge intuitive System 1 and deliberate System 2 thinking. These advanced LLMs show human-like cognitive abilities in complex tasks like math and coding.

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    Area of Science:

    • Artificial Intelligence
    • Cognitive Science

    Background:

    • Foundational Large Language Models (LLMs) excel at fast decision-making but lack deep, System 2-like reasoning.
    • Human intelligence relies on transitioning from intuitive System 1 to deliberate System 2 thinking for accuracy and bias reduction.

    Purpose of the Study:

    • To survey the development and capabilities of reasoning Large Language Models (LLMs).
    • To explore how foundational LLMs and System 2 concepts combine to create advanced reasoning models.
    • To provide a comprehensive overview of reasoning LLM construction, evolution, and evaluation.

    Main Methods:

    • Overview of foundational LLM progress and System 2 development.
    • Analysis of reasoning LLM construction and evolution.
    • Examination of core reasoning methods and benchmarks.
    • Comparison of representative reasoning LLM performance.

    Main Results:

    • Reasoning LLMs demonstrate expert-level performance in mathematics and coding.
    • These models mimic deliberate System 2 reasoning, showcasing human-like cognitive abilities.
    • Significant progress has been made in constructing and evaluating advanced reasoning LLMs.

    Conclusions:

    • Reasoning LLMs represent a significant step towards human-level intelligence.
    • Further advancements in reasoning LLMs are expected, driven by ongoing research and development.
    • This survey serves as a resource for innovation in the rapidly evolving field of AI reasoning.