大语言模型中的认知决策路由：何时快思考，何时慢思考
Cognitive Decision Routing in Large Language Models: When to Think Fast, When to Think Slow
- Independent Researchers（独立研究者）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出认知决策路由（CDR）框架，通过元认知层分析查询复杂度以动态选择快慢推理策略，在提升性能的同时降低34%计算成本，并在专业判断任务中显著改善一致性与准确率。
AI中文摘要：
大语言模型（LLMs）面临一个根本性挑战：决定何时依赖快速、直觉性的响应，何时进行更慢、更深思熟虑的推理。受丹尼尔·卡尼曼的双过程理论及其对人类认知偏差的洞见启发，我们提出了一种新颖的认知决策路由（Cognitive Decision Routing，CDR）框架，该框架根据查询特征动态确定合适的推理策略。我们的方法解决了当前模型要么对所有查询应用统一的推理深度，要么对所有查询依赖计算开销高昂的方法这一局限。我们引入了一个元认知层，通过多个维度分析查询复杂度：给定信息与所需结论之间的相关强度、领域边界跨越、利益相关者多重性以及不确定性水平。通过在多样化推理任务上的大量实验，我们证明CDR在取得优越性能的同时，与统一深度推理方法相比将计算成本降低了34%。我们的框架在专业判断任务中表现出特别优势，在专家级评估上实现了23%的一致性提升和18%的准确率改进。这项工作将认知科学原理与实际AI系统设计相连接，为大语言模型中的自适应推理提供了一种有原则的方法。
英文摘要：
Large Language Models (LLMs) face a fundamental challenge in deciding when to rely on rapid, intuitive responses versus engaging in slower, more deliberate reasoning. Inspired by Daniel Kahneman's dual-process theory and his insights on human cognitive biases, we propose a novel Cognitive Decision Routing (CDR) framework that dynamically determines the appropriate reasoning strategy based on query characteristics. Our approach addresses the current limitations where models either apply uniform reasoning depth or rely on computationally expensive methods for all queries. We introduce a meta-cognitive layer that analyzes query complexity through multiple dimensions: correlation strength between given information and required conclusions, domain boundary crossings, stakeholder multiplicity, and uncertainty levels. Through extensive experiments on diverse reasoning tasks, we demonstrate that CDR achieves superior performance while reducing computational costs by 34\% compared to uniform deep reasoning approaches. Our framework shows particular strength in professional judgment tasks, achieving 23\% improvement in consistency and 18\% better accuracy on expert-level evaluations. This work bridges cognitive science principles with practical AI system design, offering a principled approach to adaptive reasoning in LLMs.
