LLM與GraphRAG自動化CPS設計結構矩陣

• LLM Risk Assessment Framework (LRF): Domain-agnostic model classifying LLM applications along autonomy level and system impact dimensions, enabling consistent risk evaluation across engineering domains[1] • GraphSeek Architecture: Three-module system comprising Controller (LLM Agent on Semantic Plane + Non-LLM Executor on Execution Plane), Hybrid Data Store, and Adaptive Toolset; compiles semantic operations into executable graph queries[2] • Graph Data Modeling: Labeled property graphs with nodes, edges, and key-value properties; supports domain-specific attributes (e.g., BatteryModule energyDensity, DriveAssembly efficiencyRate) and relationship types (INTEGRATED_IN, OUTPUTS, INSTALLED_AT, CONNECTED_TO)[2] • Multi-Agent Security: Credit-based detectors trained on adversarial debate trajectories enable autonomous evaluation of message credibility and dynamic neighbor ranking to suppress malicious communications in LLM-based multi-agent systems[4] • Inference Hardware Optimization: Primary challenges for LLM inference are memory and interconnect rather than compute; emerging solutions include High Bandwidth Flash memory (10X capacity with HBM-like bandwidth) and Processing-Near-Memory architectures[3]

The convergence of LLMs with structured systems engineering frameworks signals a maturation phase for AI in critical domains. DSM automation through GraphRAG addresses a fundamental bottleneck in complex system design, potentially accelerating cyber-physical system development cycles. However, widespread adoption requires standardized risk assessment practices (as proposed by LRF) and robust security frameworks for multi-agent systems. The shift toward heterogeneous AI infrastructure and specialized accelerators suggests that organizations deploying LLM-based engineering tools will need to invest in adaptive infrastructure rather than relying on commodity compute. This creates opportunities for specialized tooling vendors while raising barriers to entry for smaller organizations.