# Model-Driven Requirements Configuration with Three-Valued Uncertainty Scoring

arXiv: https://arxiv.org/abs/2607.26220 · HTML: https://mlsanity.com/paper/2607.26220/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 35 references that are in the map.
- First posted on arXiv 28 Jul 2026.

## Builds on (most cited first, 12 of 12)

- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023)
- [Neurosymbolic AI: The 3rd Wave](https://mlsanity.com/paper/2012.05876/) (2020)
- [Reducing hallucination in structured outputs via Retrieval-Augmented Generation](https://mlsanity.com/paper/2404.08189/) (2024)
- [LLM Hallucinations in Practical Code Generation: Phenomena, Mechanism, and Mitigation](https://mlsanity.com/paper/2409.20550/) (2024)
- [De-Hallucinator: Mitigating LLM Hallucinations in Code Generation Tasks via Iterative Grounding](https://mlsanity.com/paper/2401.01701/) (2024)
- [Multi-Agent Debate Strategies to Enhance Requirements Engineering with Large Language Models](https://mlsanity.com/paper/2507.05981/) (2025)
- [Towards Formal Verification of LLM-Generated Code from Natural Language Prompts](https://mlsanity.com/paper/2507.13290/) (2025)
- [Leveraging Graph-RAG and Prompt Engineering to Enhance LLM-Based Automated Requirement Traceability and Compliance Checks](https://mlsanity.com/paper/2412.08593/) (2024)
- [Integrating Symbolic Execution with LLMs for Automated Generation of Program Specifications](https://mlsanity.com/paper/2506.09550/) (2025)
- [LLM-Enhanced Symbolic Control for Safety-Critical Applications](https://mlsanity.com/paper/2505.11077/) (2025)
- [TraceLLM: Leveraging Large Language Models with Prompt Engineering for Enhanced Requirements Traceability](https://mlsanity.com/paper/2602.01253/) (2026)

## Abstract (arXiv)

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. Objectives: This study aims to eliminate logical inconsistencies and enforce structural conformance in LLM-generated requirements while quantifying the LLM's pre-validation decision uncertainty within a formal domain model. Methods: We present a neuro-symbolic multi-agent architecture that operationalizes the Object-Oriented Method for Requirements Authoring and Management (OOMRAM) lattice. The LLM acts as a non-deterministic heuristic for lattice traversal, while a deterministic symbolic validator enforces all structural constraints. We introduce a three-valued (T, I, F) -- Truth, Indeterminacy, Falsity -- framework to classify and score the LLM's requirement decisions before and after validation. Results: Evaluated across 37 natural-language project visions in eleven application families, the system completely eliminated structural inconsistencies in 35 out of 37 cases (94.6%), with the remaining two containing only 6 unresolved structural errors (0.39% of decisions) due to iteration limits. Three-valued analysis revealed that 24.7% of all decisions are indeterminate -- structurally valid but discretionary choices not explicitly mandated by the stakeholder. Conclusion: Offloading structural integrity to a deterministic symbolic layer successfully guarantees structural conformance, while the three-valued classification provides a formal way to measure neural uncertainty, facilitating safe LLM deployment in formal requirements engineering.