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 model to produce a definitive answer, we query an ensemble of diverse models simultaneously. Each model responds independently to the same query. We then apply a consensus threshold — for example, requiring that 80 out of 100 models agree on the same core output before that output is presented to the user or downstream system.
This approach is inspired by blockchain networks, where multiple independent nodes validate transactions or blocks. In such systems, trust does not depend on any single node but emerges from the agreement of a decentralised network. Similarly, in LLM systems, trust can emerge from the majority agreement of an ensemble of models.
3. Benefits
Error Tolerance: Individual models may occasionally produce faulty, biased, or hallucinated outputs. Consensus validation tolerates these outliers by requiring majority agreement.
Auditable and Transparent: The consensus process can be logged and reviewed, providing an auditable trail of how outputs were validated.
Adjustable Risk Tuning: Different applications can adjust the consensus threshold based on domain risk. For example, a casual chatbot might require 60% agreement, while a legal AI tool might demand 95%+ agreement.
Complementary to Existing Techniques: Consensus validation can operate alongside RLHF (Reinforcement Learning from Human Feedback) and other alignment methods, offering an additional layer of robustness.
4. Architectures & Implementations
Homogeneous Ensembles
- Multiple instances of the same model (e.g. GPT-4 with different random seeds) can reduce stochastic variance and improve consistency.
Heterogeneous Ensembles
- Using models from different architectures (e.g. GPT-4, Claude, LLaMA, Mistral, PaLM) increases robustness against shared blind spots or systemic biases.
- Existing frameworks such as LangChain, LLaMAIndex, or custom multi-LLM orchestration pipelines can be used to implement consensus validation layers.
5. Failure Modes & Considerations
Correlated Errors: If models are trained on the same data or fine-tuned in similar ways, they may share biases or failure modes, potentially reducing the value of consensus.
Threshold Tuning: Choosing an appropriate consensus threshold involves trade-offs between safety and usability. Too high a threshold may result in no consensus being reached on difficult queries.
Cost: Running large ensembles of models can be expensive. A practical solution is to use consensus selectively — for high-risk queries or when confidence thresholds are low.
6. Applications
- Medical AI Assistants: Only release validated outputs for clinical decision support.
- Legal Research Tools: Ensure legal summaries are consensus-validated before presentation.
- AI Agents: Build safer autonomous AI agents by requiring validated intermediate decisions.
- Public-Facing Chatbots: Provide users with visible confidence scores based on consensus strength.
- Enterprise AI: Add an auditable trust layer to sensitive corporate AI deployments.
7. Conclusion
As with pneumatic tyres and their associated repair kits, the path forward in AI is not to await perfect models but to create practical safety mechanisms that manage imperfections. Blockchain consensus mechanisms offer a proven pattern for building trust in decentralised systems. This paper proposes that similar consensus validation frameworks can be applied to AI, particularly LLMs, to create more robust, transparent, and trustworthy AI systems.
By embracing decentralised consensus as an AI trust layer, we can enable safer adoption of AI across society — not by demanding flawlessness, but by designing with resilience in mind.
8. References & Further Reading
Author
Murray Aitken
Aberdeenshire, Scotland
June 2025