# Proof of Quality: A Costless Paradigm for Trustless Generative AI Model Inference on Blockchains

arXiv: https://arxiv.org/abs/2405.17934 · HTML: https://mlsanity.com/paper/2405.17934/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Discrete and text diffusion, one of 14 directions in Diffusion models.
- Builds on 12 of its 21 references that are in the map.
- First posted on arXiv 28 May 2024.

## Placed in

- Diffusion models / [Discrete and text diffusion](https://mlsanity.com/diffusion/discrete-and-text-diffusion/)

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [opML: Optimistic Machine Learning on Blockchain](https://mlsanity.com/paper/2401.17555/) (2024)
- [FHIRChain: Applying Blockchain to Securely and Scalably Share Clinical Data](https://mlsanity.com/paper/1807.03227/) (2018)
- [Agatha: Smart Contract for DNN Computation](https://mlsanity.com/paper/2105.04919/) (2021)

## Built on by (most cited first, 7 of 7)

- [Blockchain for Large Language Model Security and Safety: A Holistic Survey](https://mlsanity.com/paper/2407.20181/) (2024)
- [Design and Evaluation of Cost-Aware PoQ for Decentralized LLM Inference](https://mlsanity.com/paper/2512.16317/) (2025)
- [Adaptive and Robust Cost-Aware Proof of Quality for Decentralized LLM Inference Networks](https://mlsanity.com/paper/2601.21189/) (2026)
- [AI-Based Crypto Tokens: The Illusion of Decentralized AI?](https://mlsanity.com/paper/2505.07828/) (2025)
- [Self-Supervised Inference of Agents in Trustless Environments](https://mlsanity.com/paper/2409.08386/) (2024)
- [PoQ-Judge: A Multi-Architecture Evaluation Framework for Cost-Aware Proof-of-Quality in Decentralized LLM Inference](https://mlsanity.com/paper/2606.11196/) (2026)
- [Fortytwo: Swarm Inference with Peer-Ranked Consensus](https://mlsanity.com/paper/2510.24801/) (2025)

## Abstract (arXiv)

Generative AI models, such as GPT-4 and Stable Diffusion, have demonstrated powerful and disruptive capabilities in natural language and image tasks. However, deploying these models in decentralized environments remains challenging. Unlike traditional centralized deployment, systematically guaranteeing the integrity of AI model services in fully decentralized environments, particularly on trustless blockchains, is both crucial and difficult. In this paper, we present a new inference paradigm called proof of quality (PoQ) to enable the deployment of arbitrarily large generative models on blockchain architecture. Unlike traditional approaches based on validating inference procedures, such as ZKML or OPML, our PoQ paradigm focuses on the outcome quality of model inference. Using lightweight BERT-based cross-encoders as our underlying quality evaluation model, we design and implement PQML, the first practical protocol for real-world NLP generative model inference on blockchains, tailored for popular open-source models such as Llama 3 and Mixtral. Our analysis demonstrates that our protocol is robust against adversarial but rational participants in ecosystems, where lazy or dishonest behavior results in fewer benefits compared to well-behaving participants. The computational overhead of validating the quality evaluation is minimal, allowing quality validators to complete the quality check within a second, even using only a CPU. Preliminary simulation results show that PoQ consensus is generated in milliseconds, 1,000 times faster than any existing scheme.