用神经符号系统解决需求复用中的幻觉问题，确保生成结果结构正确。
Neuro-Symbolic Agents for Hallucination-Free Requirements Reuse
- 将需求复用转化为基于领域模型的驱动式提取过程
- 在两个应用族上实现100%覆盖率，约束违规率仅0.2%
- 适合需高可靠性与可审计性的工业级需求生成场景
面向需求编写与管理的对象化方法（OOMRAM）依赖精确标识符匹配和固定模板，难以适应多样化上下文。尽管大语言模型（LLMs）具备灵活性，但可能生成结构无效或不一致的需求组合。为此，我们提出一种神经符号多智能体系统，将需求复用重构为模型驱动的获取过程：以LLM作为非确定性启发式，在由正式OOMRAM需求格表示的确定性领域模型中导航；通过确定性的符号验证器在智能体循环中强制执行所有结构约束，从构造上杜绝幻觉需求组合。在跨两个应用家族的自主基准上评估，系统实现100%需求覆盖，约束违规率仅为0.2%。尽管对单一标准金榜的F1分数中等（0.47–0.51），但所有生成规格均结构有效且满足所有强制领域要求。该模型无关实现通过子图导航扩展至更大格结构，并提供透明审计轨迹以满足合规性要求。
原文摘要 · Abstract (English)
The Object-Oriented Method for Requirements Authoring and Management (OOMRAM) is a requirements reuse framework that relies on exact identifier matching and rigid templates, limiting its ability to adapt specifications across diverse contexts. 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 Model-Driven Elicitation process. In this paradigm, an LLM serves as a non-deterministic heuristic for traversing a 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. Evaluated on an autonomous benchmark across two application families, our system achieves 100% requirement coverage and a constraint-violation rate of only 0.2%. Although the F1-score against a single gold standard is moderate (0.47-0.51), every generated specification is structurally valid and satisfies all mandatory domain requirements. The model-agnostic implementation scales to larger lattices via subgraph navigation and provides transparent audit trails for regulatory compliance.
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