Hybrid Verification Architecture for LLM Outputs in High-Stakes Domains
A new preprint on arXiv (2605.26942v1) proposes a hybrid verification architecture for LLM outputs in data-sensitive domains. The system combines formal symbolic methods with neural semantic analysis to address hallucinations, inconsistencies, and privacy vulnerabilities. It uses logical reasoning for input verification and embedding-based semantic similarity for output validation, implemented in a parallel actor-based pipeline. This approach overcomes limitations of prompt-based self-verification, which inherits distributional biases. The architecture aims to provide complementary guarantees for structured requirements and contextual hallucination detection.
Key facts
- arXiv paper 2605.26942v1 proposes hybrid verification for LLM outputs.
- Combines formal symbolic methods with neural semantic analysis.
- Addresses hallucinations, inconsistencies, and privacy vulnerabilities.
- Uses logical reasoning for input verification with decidable guarantees.
- Employs embedding-based semantic similarity for output validation.
- Implemented in a parallel actor-based pipeline.
- Overcomes limitations of prompt-based self-verification approaches.
- Targets high-stakes domains like legal, financial, and safety applications.
Entities
Institutions
- arXiv