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Chapter 10 · 2025

Zero-knowledge LLM hallucination detection and mitigation through fine-grained cross-model consistency

Zexin Li, Zuchao Li, Xinyu Li

Abstract

Large Language Models (LLMs) are increasingly deployed in real-world applications, yet their propensity for generating factually incorrect or inconsistent information, known as hallucination, remains a significant concern. Current methods for hallucination detection often rely on external knowledge bases or require extensive human annotation, which can be costly and time-consuming. This paper proposes a novel zero-knowledge approach for LLM hallucination detection and mitigation based on fine-grained cross-model consistency. Our method leverages the inherent variability in responses from different LLMs to identify inconsistencies at a granular level, without requiring access to ground truth or external factual sources.

Eigenvector Insight — Zone III / PASF-PADE AnalysisNot part of the original paper
Eigenvector Research — Marco van Hurne
How this paper contributes to solving the Zone III problem (PASF-PADE)

This paper directly addresses one of the core structural challenges in Zone III deployments. The research on LLM Hallucination, Zero-knowledge Detection, Cross-model Consistency provides evidence-based foundations that enterprise architects cannot ignore when designing long-horizon autonomous workflows. The findings challenge the assumption that a base language model — however capable — can handle the complexity of durable, governed, multi-step execution without explicit architectural intervention. For Zone III practitioners, this paper belongs in the required reading list.

Why AI is not sufficient for Zone III without this

Zone III refers to high-complexity, high-risk, long-running agentic workflows — the class of enterprise AI deployments where a single failure can cascade across hundreds of steps. Standard AI models, trained to predict the next token, are not inherently designed for durable, governed, multi-step execution. This paper addresses one or more of the structural gaps that make Zone III deployments unsafe without explicit architectural intervention.

Topics

LLM HallucinationZero-knowledge DetectionCross-model ConsistencyHallucination MitigationFine-grained Analysis