IntelligenceFebruary 12, 202640 min read

Evidence Bundle-Enforced RAG: Mandatory Citation and Refusal Mechanisms for Trustworthy AI Responses

Shifting from 'answering' to 'answering with evidence' through a mathematical framework for hallucination reduction

Enterprise RAG reliability degrades when evidence requirements are weak. This paper introduces Evidence Bundle-Enforced RAG, where responses include mandatory citations, confidence signals, and paragraph-level provenance. When evidence is insufficient, the system can refuse to answer instead of fabricating content. We present a mathematical model for evidence sufficiency scoring, hallucination control, trust dynamics, and recursive improvement loops. In enterprise document-QA evaluations, hallucination rate was reduced from 23.7% to 3.2%.

evidence-bundlesRAGhallucination-reductiontrust-engineeringcitationrefusal-mechanisms
IntelligenceJanuary 14, 202624 min read

Why Evidence Bundles Stabilize RAG Accuracy: A Variance Reduction Framework

Proving that bundled evidence reduces hallucination rate exponentially and establishing cohesion-based answer refusal thresholds

RAG reliability depends strongly on evidence quality and cohesion. When retrieved passages are topically scattered, model outputs are more likely to hallucinate to fill coherence gaps. This paper models hallucination rate as `H(e) = H_base * exp(-lambda * density(e))`, analyzes how bundled retrieval reduces answer variance as cohesion increases, and derives cohesion thresholds for refusal behavior under low-evidence conditions. Across 8,400 governance queries, evidence bundles reduced hallucination from 12.3% to 2.1%.

evidence-bundlesrag-stabilityhallucinationvariance-reductioncohesion-scoreanswer-refusal