A Neuro-Symbolic Strategy to Support the Model-Driven Design of Systems Engineering Experiments
TL;DR
A neuro-symbolic framework combining LLM-driven elicitation constrained by a rule-based reasoner fed by an ontology-compliant knowledge graph is proposed and results indicate that the framework reliably prevents hallucinations from propagating into formal specifications.
Abstract
: The design and specification of experiments in Model-Based Systems Engineering is challenging: state-of-the-art tools are deemed either precise, but too cumbersome or too imprecise due to natural-language descriptions that lack formal semantics. This is compounded by the high complexity of systems, especially in safety-critical domains. Large Language Models (LLMs) offer a promising avenue for automating the elicitation step, but their probabilistic nature precludes unmediated use: hallucinations cannot be allowed to propagate into formal artifacts. We propose a neuro-symbolic framework combining LLM-driven elicitation constrained by a rule-based reasoner fed by an ontology-compliant knowledge graph. A deterministic orchestrator drives an elicitation loop where the symbolic engine poses context-sensitive questions, the LLM proposes candidate answers, and every candidate is validated against formal domain constraints before acceptance. We present a proof-of-concept implementing the proposed framework and an empirical evaluation across three case studies using four state-of-the-art LLMs. Results indicate that the framework reliably prevents hallucinations from propagating into formal specifications.