ENGINEERING BLOG
Technical research and engineering insights from the team building the operating system for responsible AI operations.
121 articles · Published by MARIA OS
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%.
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%.
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Complete list of all 121 published articles. EN / JA bilingual index.
121 articles
All articles reviewed and approved by the MARIA OS Editorial Pipeline.
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