Agentic Governance and Adversarial Verification for Policy-Constrained LLM Healthcare Appeal Generation

Excerpt

arXiv:2609.27844v1 Announce Type: cross Abstract: Claim denial management costs U.S. healthcare approximately $260 billion annually in administrative overhead. Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) can produce fluent clinical text, but single-agent architectures fail in high-stakes healthcare: they introduce unsupported clinical details and lose the logical structure of hierarchical payer policy. We propose AGVF (Agentic Governance and Adversarial Verification Fra