MoT: Modularization-of-Thought Prompting for Effective Code Generation

2026-07-20 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Expert, extended

Summary

Modularization-of-Thought (MoT) is a novel prompting technique designed to enhance Large Language Model (LLM) code generation by applying software modularization principles. MoT decomposes complex programming problems into smaller, independent reasoning steps, structuring this process with a Multi-Level Reasoning (MLR) Graph that hierarchically organizes tasks. This approach improves LLM comprehension of complex problems and aligns reasoning steps with generated code. Extensive experiments on GPT-4o-mini and DeepSeek-R1, across six datasets including HumanEval and MBPP, demonstrated MoT's significant performance gains. MoT achieved Pass@1 scores from 58.1% to 95.1%, consistently outperforming six baseline techniques like Chain-of-Thought (CoT) and Structured Chain-of-Thought (SCoT). Furthermore, MoT proved more cost-efficient, with an average generation time of 5 seconds on HumanEval, notably faster than CodeCoT's 13 seconds.

Key takeaway

For Machine Learning Engineers aiming to improve LLM code generation, MoT provides a superior, cost-effective approach. You should adopt MoT's modular reasoning and Multi-Level Reasoning Graph to decompose complex programming problems. This will significantly enhance code accuracy and maintainability, reducing debugging efforts and overall computational costs compared to traditional linear prompting methods.

Key insights

MoT improves LLM code generation by structuring reasoning with modularization principles and a Multi-Level Reasoning Graph.

Principles

• Decompose complex problems into independent sub-tasks.
• Hierarchical design enhances system flexibility.
• Explicitly define task boundaries and dependencies.

Method

MoT generates an MLR Graph (high, intermediate, detailed levels) from problem descriptions, then guides progressive code generation using this hierarchical structure.

In practice

• Structure LLM prompts with hierarchical MLR Graphs.
• Decompose coding tasks into high, intermediate, detailed levels.
• Embed task purpose, rationale, and strategy in each node.

Topics

• MoT Prompting
• Code Generation
• LLM Performance
• Multi-Level Reasoning Graph
• Software Modularization
• Prompt Engineering

Best for: Research Scientist, AI Engineer, AI Scientist, Machine Learning Engineer, Prompt Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.