llm-sysml-alignment

# LLM-SysML Alignment LLM-assisted methodology for semantic alignment and integration of SysML v2 models in collaborative Model-Based Systems Engineering (MBSE). ## Problem Statement Cross-organizational collaboration in MBSE faces challenges in achieving semantic alignment across independently developed system models. Different organizations use different naming conventions, model structures, and domain-specific terminology, making integration difficult. ## Solution Approach Structured, prompt-driven approach leveraging: - **SysML v2 constructs**: alias, import, metadata extensions - **LLM capabilities**: semantic matching, syntax verification, traceability - **Iterative process**: model extraction → semantic matching → verification ## Core Methodology ### Step 1: Model Extraction Extract semantic information from SysML v2 models: ```python # Key elements to extract: - Element names and aliases - Relationships and dependencies - Domain-specific terminology - Metadata and annotations ``` ### Step 2: Semantic Matching Use LLM for semantic alignment: ```markdown Prompt structure: 1. Identify equivalent elements across models 2. Detect semantic similarities despite naming differences 3. Generate alignment mappings 4. Create traceability links ``` ### Step 3: Verification and Integration Verify alignment consistency: ```python Verification checks: - Syntax correctness (SysML v2 compliant) - Semantic consistency (equivalent meanings) - Traceability (alignment rationale documented) - Completeness (all elements covered) ``` ## SysML v2 Constructs Used ### Alias ```sysml alias ModelA.Part as ModelB.Component; ``` ### Import ```sysml import ModelA::*; import ModelB::*; ``` ### Metadata Extensions ```sysml metadata alignmentSource = "ModelA"; metadata alignmentConfidence = 0.95; ``` ## Workflow Example **Scenario**: Two companies developing subsystem models for a larger system. ```markdown Model A (Company 1): - EngineSubsystem - FuelSystem - PowerControl Model B (Company 2): - PropulsionModule - FuelManagement - EnergyRegulator Alignment Process: 1. LLM identifies semantic equivalents 2. Creates alias mappings 3. Generates import statements 4. Adds metadata for traceability ``` ## LLM Prompt Patterns ### Semantic Extraction Prompt ```markdown Extract semantic information from SysML v2 model [MODEL]: 1. Identify core concepts and their domain 2. List element relationships 3. Document naming conventions used 4. Extract domain-specific terminology ``` ### Alignment Matching Prompt ```markdown Match elements between Model A and Model B: 1. Identify equivalent elements by semantics (not names) 2. Generate alias mappings 3. Document alignment rationale 4. Flag ambiguous matches for human review ``` ### Verification Prompt ```markdown Verify alignment correctness: 1. Check SysML v2 syntax compliance 2. Verify semantic equivalence 3. Ensure traceability completeness 4. Identify missing alignments ``` ## Best Practices 1. **Iterative refinement**: LLM alignment may need multiple iterations 2. **Human verification**: Flag ambiguous matches for review 3. **Metadata traceability**: Always document alignment rationale 4. **Soft alignment**: Use aliases instead of renaming 5. **Domain context**: Provide domain-specific context to LLM ## Key Findings (from Research) - LLMs effectively assist in semantic alignment across engineering models - SysML v2 provides robust framework for model integration - Structured prompts improve alignment accuracy - Traceability essential for maintaining alignment over time - Soft alignment (aliases) preferred over hard renaming ## Applications - Cross-company system integration - Legacy model modernization - Domain-specific model translation - Multi-team collaborative MBSE - System of systems integration ## Related Skills - **arxiv-search**: Search for latest MBSE papers - **kg-research-workflow**: Import papers to knowledge graph - **skill-creator**: Create new skills from research ## Source Paper **LLM-Assisted Semantic Alignment and Integration in Collaborative Model-Based Systems Engineering** - arxiv ID: 2508.16181 - Authors: Li, Zirui et al. - Published: 2026 ## Notes - Requires SysML v2 knowledge - LLM prompts should be domain-specific - Alignment confidence varies by domain complexity - Human review essential for safety-critical systems