ENGINEERING BLOG
Technical research and engineering insights from the team building the operating system for responsible AI operations.
121 articles · Published by MARIA OS
Why controlling RAG accuracy through responsibility structure outperforms Top-k optimization alone
Many RAG systems optimize retrieval quality primarily through Top-k tuning and embedding similarity. This paper adds a governance-oriented approach: responsibility-tiered gates that adjust validation intensity by risk classification. The framework reports an 82% hallucination-rate reduction on enterprise document corpora while maintaining sub-second response times for low-risk queries.
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%.
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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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