Dynamic Master Logic (DML) provides a hierarchical framework for representing system behavior by linking functional objectives to underlying structural elements. However, DML construction typically relies on expert interpretation of technical documentation, limiting scalability for complex systems.
arXiv:2608. 12304v1 Announce Type: new Abstract: Dynamic Master Logic (DML) provides a hierarchical framework for representing system behavior by linking functional objectives to underlying structural elements.
arXiv:2606. 31614v1 Announce Type: cross Abstract: Engineering specifications such as interlocks, alarm rationalization tables, and cause-and-effect (C&E) matrices remain central to process control and safety, yet their creation is still predominantly manual, document-driven, and prone to inconsistency.
SysML v2's textual syntax enables compiler-based validation of model structure and language conformance. However, semantic mistakes that preserve syntactic validity but violate domain rules cannot be detected through compilers.
arXiv:2607. 22153v1 Announce Type: cross Abstract: Industrial health management increasingly relies on heterogeneous information sources, including condition monitoring systems, supervisory control and data acquisition systems, maintenance records, inspection results, and prognostic models.
This paper presents a black-box evaluation framework to systematically assess the ability of Large Language Models (LLMs) to generate Design Structure Matrices (DSMs) from structured technical documentation. Motivated by the closed-source nature of current Auto-DSM pipelines, the framework introduces a reproducible methodology that benchmarks generated DSMs (GEN-DSMs) against manually validated ground-truth matrices (GT-DSMs).
arXiv:2607. 05985v1 Announce Type: new Abstract: This paper presents a black-box evaluation framework to systematically assess the ability of Large Language Models (LLMs) to generate Design Structure Matrices (DSMs) from structured technical documentation.
LogicTree-RAG is a retrieval‑augmented generation framework that uses a hierarchical logic tree to guide the creation of long‑form patent drafts. Each node in the tree represents a technical element and is built through evidence‑guided recursive generation, while a hybrid traversal maps the tree into patent sections for balanced, controllable output. Experiments show that this logic‑centric approach improves content quality, language conformity, and token efficiency compared to strong LLM baselines.
This paper presents an ontology-supported approach to tackle the complexity of the Robustness Validation (RV) process of automotive electrical/electronic (E/E) components. The approach uses formalized...
arXiv:2607. 08288v1 Announce Type: cross Abstract: In critical infrastructure, operational technology environments often cannot be actively scanned, and yet active system feedback is needed for risk assessment and compliance.
arXiv:2607. 26220v1 Announce Type: cross Abstract: Context: Large Language Models (LLMs) offer natural-language flexibility for automated requirements elicitation but frequently generate structurally invalid requirements and logical inconsistencies, lacking formal correctness guarantees.
The paper introduces ChemOntoRule, a symbolic core designed to aid AI in solving school‑level chemistry problems. It uses a task‑centric ontology built around the specific concepts and procedures needed for a defined set of problems, combined with deterministic Python rules for electronic structure, periodic trends, oxidation states, and related reasoning patterns. Evaluated on 300 human‑authored problems, the system matched 296 reference answers (98.67%), with the ontology‑driven rules covering 269 problems and achieving 98.88% accuracy.
arXiv:2607. 17963v1 Announce Type: new Abstract: Ontology extension refers to the process of enriching an existing ontology in response to emerging requirements, making it more complete.