In-context Learning (ICL) enables large language models (LLMs) to tackle downstream tasks through sophisticated prompting and high-quality demonstrations. However, this traditional ICL paradigm shows limitations when facing complex mathematical reasoning tasks, primarily due to its heavy dependence on example quality and the necessity for human intervention in challenging scenarios. To address these limitations, this paper presents HiAR-ICL, a \textbf{Hi}gh-level \textbf{A}utomated \textbf{R}easoning paradigm in \textbf{ICL} that shifts focus from specific examples to abstract thinking patterns, extending the conventional concept of context in ICL. HiAR-ICL introduces five atomic reasoning actions as fundamental components for constructing chain-structured patterns. Using Monte Carlo Tree Search, we explore reasoning paths and construct thought cards to guide subsequent inference. We then develop a cognitive complexity framework that dynamically matches problems with appropriate thought cards. Experimental results demonstrate HiAR-ICL's effectiveness, achieving state-of-the-art accuracy (79.6$\%$) on the MATH benchmark with Qwen2.5-7B-Instruct, surpassing GPT-4o (76.6$\%$) and Claude 3.5 (71.1$\%$).

本研究解决了传统的上下文学习在复杂数学推理任务中的局限性，如对示例质量的高度依赖和在挑战场景中对人类干预的需要。提出的HiAR-ICL范式转变了研究焦点，从具体示例转向抽象思维模式，并通过五个基本推理动作构造链状模式，利用蒙特卡洛树搜索探索推理路径，从而实现了在MATH基准测试中达到79.6%的前沿准确率，超越了现有模型。

超越示例：基于MCTS的上下文学习中的高级自动推理范式