Zero Hallucination, by Construction: Hallucination-Aware Layered Oversight for Trustworthy Enterprise AI
Enterprises will not deploy AI agents they cannot trust, and the most-cited reason for distrust is hallucination: confident, fluent output that is simply not true. The common response is to wait for a model that does not hallucinate. We argue that this is the wrong target. Large language models are, by construction, capable of generating unsupported text, and no amount of scale removes the possibility; a faithfulness judge bolted onto a raw model catches some errors but still ships others, and even well-curated retrieval pipelines have been shown to fabricate citations. We reframe the goal: "z
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 57%The AI context gap: Enterprise AI organizations have a trust problem, not a retrieval problem — and most are still building the fix →
- PossiblePossibly related (embedding) · 52%Prompt injection is exploiting enterprise AI's biggest design flaws by targeting agents, RAG pipelines and model routers →
- LinkedLinked via arxiv author · 85%Bogdan Raduta →
“Zero Hallucination, by Construction: Hallucination-Aware Layered Oversight for Trustworthy Enterprise AI”
- LinkedLinked via arxiv author · 85%Horia Velicu →
“Zero Hallucination, by Construction: Hallucination-Aware Layered Oversight for Trustworthy Enterprise AI”
- LinkedLinked via arxiv author · 85%Alexandru Preda →
“Zero Hallucination, by Construction: Hallucination-Aware Layered Oversight for Trustworthy Enterprise AI”
- LinkedLinked via arxiv author · 85%Serban Chiricescu →
“Zero Hallucination, by Construction: Hallucination-Aware Layered Oversight for Trustworthy Enterprise AI”