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While Large Language Models (LLMs) offer the flexibility to overcome this bottleneck, they introduce the risk of generating structurally invalid or inconsistent requirement combinations. To address this tension, we present a neuro-symbolic multi-agent system that re-conceptualizes requirements reuse as a \\textbf{Model-Driven Elicitation process}. In this paradigm, an LLM serves as a \\textbf{non-deterministic heuristic} for traversing a \\textbf{deterministic domain model} represented by a formal OOMRAM requirement lattice. A deterministic, symbolic validator enforces all structural constraints within the agent loop, effectively eliminating hallucinated requirement combinations by construction. 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