dsm-llm-modularization

name

dsm-llm-modularization

description

LLM-based Design Structure Matrix (DSM) modularization methodology. Use when partitioning complex systems into cohesive modules, optimizing system architecture, or applying LLMs to combinatorial engineering problems. Activation triggers: DSM, design structure matrix, system modularization, architecture decomposition, LLM combinatorial optimization, semantic alignment hypothesis, engineering design optimization.

DSM Modularization with Large Language Models

LLM-based approach to Design Structure Matrix modularization achieving near-reference quality within 30 iterations without specialized optimization code. Introduces the semantic-alignment hypothesis governing when domain knowledge helps or hurts LLM-based optimization.

Metadata

• Source: arXiv:2604.28018
• Authors: Shuo Jiang, Jianxi Luo
• Published: 2026-04-30
• Subjects: cs.CE (Computational Engineering); cs.AI

Core Methodology

Key Innovation

DSM modularization partitions system elements into cohesive modules — a fundamental combinatorial challenge in engineering design. This paper shows that LLMs can solve DSM modularization through iterative prompting, without requiring specialized optimization code.

Technical Framework

Three-stage LLM-based modularization:

1. Input Representation

   • Convert DSM (N×N dependency matrix) into natural language description
   • Include element names, dependency strengths, and constraints
   • Best practice: Include raw matrix structure + semantic labels

2. Objective Formulation

   • Frame modularization as minimizing inter-module dependencies
   • Use standard metrics: Minimum Description Length (MDL), clustering coefficient
   • Best practice: Provide explicit objective function in natural language

3. Solution Pool Design

   • Maintain a pool of candidate solutions across iterations
   • LLM generates new candidates, evaluates against pool
   • Best practice: Pool size 5-10, retain top-k by objective value

Critical Finding: Semantic-Alignment Hypothesis

Counterintuitive result: Domain knowledge, beneficial in DSM sequencing, consistently impairs performance on DSM modularization for complex DSMs.

Hypothesis: LLM effectiveness with domain knowledge depends on semantic alignment between:

• The LLM's functional priors (what it "knows" about the domain)
• The optimization objective (structural vs. functional)

When knowledge helps: The domain semantics align with the optimization goal When knowledge hurts: The LLM's functional priors conflict with structural optimization objectives

Testable condition: Before adding domain knowledge to LLM prompts, verify whether the knowledge supports or contradicts the mathematical objective.

Implementation Guide

Step 1: DSM Construction

• Build N×N matrix of element dependencies
• Weight edges by dependency strength
• Validate matrix completeness

Step 2: LLM Prompt Design

System: You are an expert in system architecture modularization.
Task: Partition these {N} system elements into cohesive modules.
Input: {DSM_description_with_element_names_and_dependencies}
Objective: Minimize inter-module dependencies (total coupling between modules).
Constraints: {max_modules, min_module_size, etc.}
Format: Return module assignments as JSON.

Step 3: Iterative Optimization

• Run 30 iterations of LLM prompting
• Each iteration: present current best + ask for improvement
• Track objective value across iterations
• Convergence typically within 20-30 iterations

Step 4: Validation

• Compare against reference solutions (if available)
• Compute modularity metrics (MDL, clustering coefficient)
• Evaluate semantic coherence of modules

Pitfalls

• Semantic misalignment: Adding domain knowledge to prompts can degrade results if the knowledge conflicts with structural optimization. Test both with and without domain context.
• LLM variability: Different backbone LLMs produce different quality results. Test across 2-3 models.
• Prompt sensitivity: Input representation format significantly impacts results. Use ablation studies to find optimal format.
• Scale limits: Method validated on DSMs up to moderate size. Very large DSMs (N > 200) may need hierarchical decomposition.

Applications

• System architecture design
• Software modularization
• Product family planning
• Organization design
• Supply chain clustering

Related Skills

• modern-systems-engineering-patterns
• emergent-systems-design
• agent-first-bootstrap