Beyond Examples: High-level Automated Reasoning Paradigm in In-Context
  Learning via MCTS
Authors: name: Jinyang Wu, name: Mingkuan Feng, name: Shuai Zhang, name: Feihu Che, name: Zengqi Wen, name: Jianhua Tao

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 High-level Automated Reasoning paradigm
in 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%).