Neuro-Symbolic Verification of LLM Outputs for Data-Sensitive Domains (extended preprint)

Technical Summary: Neuro-Symbolic Verification of LLM Outputs for Data-Sensitive Domains

Problem Statement
Large Language Models (LLMs) face fundamental reliability challenges when deployed in high-stakes, data-sensitive domains such as medical diagnosis, legal review, and regulatory affairs. These challenges include hallucinations (generating credible but false information), inconsistencies, and privacy vulnerabilities. In these contexts, errors carry legal, financial, or safety consequences, rendering the 3–10% hallucination rates observed in state-of-the-art models unacceptable. Furthermore, strict regulatory and institutional constraints often mandate local data processing to ensure data sovereignty, limiting the utility of cloud-based APIs and general-purpose LLMs. Existing approaches, such as retrieval-augmented generation (RAG) or chain-of-thought prompting, lack the necessary guarantees for specialized settings, while self-verification methods inherit the distributional biases of the models they attempt to verify.

Methodology
The paper proposes a hybrid verification architecture that combines formal symbolic methods with neural semantic analysis to provide complementary guarantees. The system is designed around four core principles: local-first processing, modular separation of symbolic and neural components, actor-based fault isolation, and type-aware claim verification.

1. Symbolic Input Verification (Pre-processing):
   The architecture employs tableaux-based logic to validate input constraints before any LLM inference occurs. This method decomposes validation requirements into a configurable hierarchy of declarative conditions. Core conditions evaluate atomic predicates (e.g., metadata presence), while higher-order meta-conditions express set-theoretic relationships (e.g., cumulative obligations or alternative sufficiency paths). This approach provides decidable, sound, and complete guarantees for structured requirements, acting as a "hard stop" for critical violations or a graded warning system for minor mismatches.

2. Actor-Based Concurrency:
   To ensure robustness and fault isolation, the system utilizes the actor model (implemented via Elixir on the Erlang VM). Verification pipelines execute multiple heterogeneous analysis streams (symbolic rule validation, entity extraction, neural embedding comparisons) concurrently without shared mutable state. Supervisor actors monitor worker processes, automatically restarting failed components (e.g., neural inference timeouts) without corrupting the broader symbolic validation state.

3. Hybrid Output Verification (Post-processing):
   The architecture implements a type-aware verification strategy for LLM-generated content:

   • Structured Entities: For deterministic data (dates, identifiers, numeric values), the system uses symbolic pattern matching and regex-based comparison to provide exact verification.
   • Free-Text Claims: For semantic content, the system employs embedding-based semantic similarity (using multilingual sentence encoders) to detect contextual hallucinations where formal methods lack expressiveness.
   • Parallel Integration: Symbolic and neural processing streams run in parallel. In retrieval scenarios, symbolic filtering narrows the search space (e.g., by section type or report ID) before neural ranking, reducing noise and token usage for locally deployed models.

4. Privacy-Preserving Design:
   The system enforces "local-first" processing, ensuring all inference, embedding, and verification occur on local hardware. Sensitive data is isolated within dedicated supervision subtrees, and vector embeddings serve as lossy transformations for indexed collections, ensuring information-theoretic protection.

System Implementation: HAIMEDA
The architecture is instantiated in HAIMEDA (Hybrid AI for Medical Device Assessment), a system developed through Action Design Research (ADR) with a domain expert. HAIMEDA is designed for the German legal context, where a sworn expert witness must produce damage assessment reports under strict confidentiality and data sovereignty constraints. The system integrates:

• Report Authoring Module (RAM): Handles generation via fine-tuned Llama3-DiscoLeo-Instruct-8B models.
• Research and Investigation Module (RIM): Executes hybrid narrowing for domain knowledge retrieval.
• Information Integrity Verification Module (IIVM): Performs multi-stage verification against source material.

Results
Evaluation was conducted on consumer-grade hardware (AMD Ryzen 9, NVIDIA RTX 4090) to reflect realistic local deployment constraints.

• Hallucination Detection: The system achieved detection rates of over 83% for structured entities (via symbolic checks) and 72% for semantic fabrications (via neural similarity). Pre-processing logic achieved 100% detection of missing required conditions.
• Workflow Efficiency: Practical evaluation with a domain expert revealed a 30% reduction in total report creation time. Specifically, content writing time decreased by 50%, and document-based research time decreased by 33%, though final review time increased due to the verification process.
• System Usability: The system received a System Usability Scale (SUS) score of 62.5.

Significance and Claims
The paper claims that neuro-symbolic architectures can provide principled safeguards for LLM deployment in data-sensitive domains by addressing the limitations of purely neural approaches. The primary contribution is a model-agnostic architecture that grounds formal guarantees in symbolic validation rather than relying on a single base model's capabilities. By separating verification tasks based on their nature (decidable constraints vs. semantic ambiguity), the system offers a scalable solution for ensuring information integrity without compromising data sovereignty. The authors emphasize that this approach moves beyond point solutions to offer a generalizable architectural pattern for hybrid AI, enabling fault-isolated, locally deployable quality-control pipelines that are essential for regulated industries. The paper acknowledges limitations, including dependence on domain-specific rule coverage and the need for future work on cross-domain transfer and ablation studies.