Claim of Authorship

"This article proposes the concept of applying decentralised, blockchain-inspired consensus mechanisms to the validation of Large Language Model (LLM) outputs as a method to improve reliability and trustworthiness in AI systems. The analogy of a 'puncture repair kit' for LLMs — using ensembles of models to confirm output validity — was independently conceived and articulated by the author, Murray Aitken, in June 2025. This document serves as an original public record of the idea and associated framing."

Abstract

As Large Language Models (LLMs) become increasingly embedded in everyday tools and decision-making systems, concerns about their reliability and trustworthiness grow. Inspired by blockchain’s use of decentralised consensus to ensure trust, this paper proposes a multi-model consensus validation framework for AI outputs. By querying ensembles of diverse AI models and validating outputs through majority agreement, we can create AI systems that are more robust, auditable, and transparent. This approach offers a practical “repair kit” for AI imperfections, enabling more confident deployment of LLM-based assistants across sensitive and high-stakes domains.

1. Introduction

Large Language Models (LLMs) such as GPT-4, Claude, and LLaMA have demonstrated remarkable capabilities in generating human-like text, assisting with reasoning, and powering new AI tools. However, these models remain inherently stochastic — capable of producing plausible-sounding but incorrect or misleading outputs. As AI tools are deployed in increasingly sensitive domains such as healthcare, finance, and law, building user trust in their outputs becomes a critical challenge.

Rather than expecting perfect accuracy from any single AI model — a goal that remains elusive — we can draw inspiration from how other technologies manage reliability. Pneumatic tyres, for example, offered vast improvements over wooden wheels but introduced new failure modes (punctures). The solution was not to abandon pneumatic tyres, but to create repair kits, monitoring tools, and safety processes that mitigate risk while allowing progress. This paper proposes a similar mindset for AI: using decentralised, blockchain-inspired consensus mechanisms as a practical “repair kit” for LLM reliability.

2. Concept: Multi-Model Consensus Validation

The core idea is simple: instead of relying on a single AI mode...

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