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Proceedings of the AAAI Conference on Artificial Intelligence

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Intention Chain-of-Thought Prompting with Dynamic Routing...
Shen Li, Li · 2026-03-14 · via Proceedings of the AAAI Conference on Artificial Intelligence

Authors

  • Shen Li Chongqing University
  • Li Huang Chongqing University
  • Shaoxiong Zhan Tsinghua University
  • Weifeng Sun Chongqing University
  • Tao Yin Chongqing University
  • Zhongxin Liu Zhejiang University
  • Meng Yan Chongqing University

DOI:

https://doi.org/10.1609/aaai.v40i1.37030

Abstract

Large language models (LLMs) exhibit strong generative capabilities and have shown great potential in code generation. Existing chain-of-thought (CoT) prompting methods enhance model reasoning by eliciting intermediate steps, but suffer from two major limitations: First, their uniform application tends to induce overthinking on simple tasks. Second, they lack intention abstraction in code generation, such as explicitly modeling core algorithmic design and efficiency, leading models to focus on surface-level structures while neglecting the global problem objective. Inspired by the cognitive economy principle of engaging structured reasoning only when necessary to conserve cognitive resources, we propose RoutingGen, a novel difficulty-aware routing framework that dynamically adapts prompting strategies for code generation. For simple tasks, it adopts few-shot prompting; for more complex ones, it invokes a structured reasoning strategy, termed Intention Chain-of-Thought (ICoT), which we introduce to guide the model in capturing task intention, such as the core algorithmic logic and its time complexity. Experiments across three models and six standard code generation benchmarks show that RoutingGen achieves state-of-the-art performance in most settings, while reducing total token usage by 46.37% on average across settings. Furthermore, ICoT outperforms six existing prompting baselines on challenging benchmarks.

How to Cite

Li, S., Huang, L., Zhan, S., Sun, W., Yin, T., Liu, Z., & Yan, M. (2026). Intention Chain-of-Thought Prompting with Dynamic Routing for Code Generation. Proceedings of the AAAI Conference on Artificial Intelligence, 40(1), 650–658. https://doi.org/10.1609/aaai.v40i1.37030

Issue

Section

AAAI Technical Track on Application Domains I

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