Meme-Aware Legal and Policy RAG for Explainable Governance

Legal, compliance, and enterprise-policy retrieval systems must explain not only which source supports an answer, but also which recurring phrase, standard, exception, or interpretive frame shaped the answer. Conventional retrieval-augmented generation can cite documents while still missing the lineage of policy language across runbooks, legal memoranda, contracts, incident reports, and governance repositories. This paper proposes Meme-Aware Legal and Policy RAG (MAP-RAG), a synthetic architecture that tracks phrase-level policy memes as auditable units in a retrieval graph. MAP-RAG combines hybrid semantic-relational retrieval, critic-guided documentation maintenance, distributed RAG, contract-mediated agent tools, privacy-preserving evidence views, and drift-aware phrase scoring. It extends Retrieval-Grounded Documentation Agents for Enterprise Compliance Evidence with phrase-level explanation packs, extends Ideological Drift Detection in Governed Enterprise Knowledge Bases with legal and policy influence graphs, and extends Contract-Driven Multi-Agent Incident Response for Cloud-Native Platforms with governance-specific tool contracts. In a simulated legal and enterprise-policy benchmark, MAP-RAG improves citation-supported answer faithfulness from 0.74 to 0.91, raises phrase-lineage explanation coverage from 0.38 to 0.86, and reduces unsupported policy actions by 52.8% relative to a strong RAG-only baseline.