Scaling AI: Incentivizing Quality in Distributed Language Models

Author: Denis Avetisyan

A new mechanism for ensuring reliable and efficient performance in decentralized AI systems is proposed, addressing the challenges of cost and quality in large language model inference.

The pursuit of enhanced output quality in large language models encounters diminishing returns, as gains become increasingly expensive; while model size correlates with performance improvements, computational cost escalates disproportionately, ultimately decreasing the quality-to-cost ratio and suggesting that incentive structures focused solely on quality may inadvertently favor inefficient, excessively large models.

This paper introduces a cost-aware Proof of Quality (PoQ) system for decentralized LLM inference, balancing performance with computational cost and evaluator reliability using blockchain technology.

Decentralized large language model (LLM) inference promises open and censorship-resistant AI access, yet current verification methods struggle with scalability and economic sustainability. This paper, ‘Design and Evaluation of Cost-Aware PoQ for Decentralized LLM Inference’, introduces a novel Proof of Quality (PoQ) framework that integrates computational cost directly into the incentive mechanism for both inference and evaluation nodes. Results demonstrate that rewarding both quality and efficiency leads to improved resource allocation and demonstrably favors high-performance, low-latency models, as confirmed through extensive simulations and comparative analysis of diverse LLM and evaluator architectures. Could this cost-aware approach unlock a truly viable path toward economically sustainable, decentralized AI infrastructure?

The Inevitable Challenge of Reliable AI Assessment

The rapid integration of Large Language Models (LLMs) into diverse applications – from customer service chatbots and content creation tools to complex data analysis pipelines – presents a critical hurdle: ensuring the reliability of their outputs. While LLMs demonstrate remarkable capabilities in generating human-quality text, consistently evaluating that text for accuracy, relevance, and safety proves remarkably difficult. Current evaluation methods often rely on human annotators, a process that is both costly and susceptible to subjective biases. Automated metrics, while scalable, frequently struggle to capture the subtleties of language, failing to identify nuanced errors or logical inconsistencies. This disconnect between deployment speed and robust evaluation poses a significant challenge, potentially leading to the widespread adoption of systems that generate misleading, harmful, or simply incorrect information, thereby undermining trust and hindering responsible innovation in the field of artificial intelligence.

The assessment of Large Language Model (LLM) performance is frequently hampered by the limitations of conventional evaluation techniques. Reliance on human judgment introduces subjectivity and scalability issues, while automated metrics often struggle to identify subtle but critical errors in reasoning or factual accuracy. Furthermore, the cost associated with expert annotation can be prohibitive, especially when evaluating LLMs across diverse tasks and languages. This creates a paradox: as LLMs become more sophisticated, pinpointing their failings becomes increasingly difficult and resource-intensive, leading to unreliable benchmarks and hindering progress towards truly robust and trustworthy artificial intelligence. The inability to consistently and accurately measure LLM capabilities ultimately undermines their responsible deployment and limits their potential impact.

Blockchain inference verification paradigms vary significantly in computational cost and overhead, ranging from lightweight quality assessments to computationally intensive proof generation or complete lack of verification.

A Paradigm Shift: Introducing Proof of Quality

Proof of Quality (PoQ) represents a departure from traditional Large Language Model (LLM) verification methods which often focus on scrutinizing the internal inference process. Instead, PoQ centers on evaluating the outputs of LLMs using dedicated, computationally efficient evaluator models. This approach avoids the complexities of tracing and validating each step of the inference pipeline, offering a potentially scalable solution for quality assessment. By focusing solely on the observable output, PoQ enables a more direct and readily quantifiable measure of LLM performance, independent of the underlying model architecture or computational graph.

The Proof of Quality (PoQ) methodology utilizes Bi-Encoder and Cross-Encoder models for efficient assessment of Large Language Model (LLM) outputs. Bi-Encoders generate vector embeddings for both candidate outputs and reference texts, enabling rapid comparison via cosine similarity or other distance metrics. Cross-Encoders, conversely, process the candidate output and reference text together, providing a more nuanced but computationally intensive evaluation. Both model types avoid the need to scrutinize the LLM’s internal inference process, focusing instead on direct comparison to established references to determine output quality.

Bi-Encoder models utilized in Proof of Quality (PoQ) systems demonstrate improved performance in semantic similarity calculations through training on benchmarks like Semantic Textual Similarity Task Suite (STSTS). STSTS provides a diverse dataset of sentence pairs with human-annotated similarity scores, enabling the Bi-Encoder to learn more robust representations of semantic meaning. This training process optimizes the model’s ability to accurately assess the relatedness of candidate LLM outputs to reference texts, crucial for evaluating output quality without relying on complex inference verification. The resulting models exhibit enhanced precision in determining the degree of semantic overlap between texts, thereby increasing the reliability of the PoQ system’s quality assessment.

The cost-aware PoQ pipeline processes text from datasets like SQuAD and CNN/DailyMail through inference and evaluation nodes to generate quality scores and cost-based rewards.

Incentivizing Efficiency: A Cost-Aware Approach to Quality

The Proof of Quality (PoQ) system incorporates a Cost-Aware Incentive Mechanism designed to reward nodes – both those performing inference and those evaluating outputs – based on their quality-to-cost ratio. This mechanism moves beyond simply rewarding accuracy; it prioritizes efficient performance by factoring in computational cost alongside quality metrics. Nodes are incentivized to minimize resource expenditure while maintaining high output quality, creating a system where efficiency is as valuable as correctness. This is achieved through a reward structure that directly correlates with the ratio of quality achieved relative to the computational resources consumed during both inference and evaluation processes.

Objective output accuracy is quantified through the Ground Truth F1 Score, a metric derived from Token-Level Precision and Recall. Precision calculates the proportion of correctly predicted tokens among all tokens predicted by the model, while Recall determines the proportion of correctly predicted tokens among all ground truth tokens. The F1 Score represents the harmonic mean of Precision and Recall, providing a balanced measure of a model’s accuracy. Specifically, the $F_1$ score is calculated as $2 (Precision Recall) / (Precision + Recall)$. Utilizing token-level analysis allows for granular assessment of model performance beyond simple overall accuracy, contributing to a more nuanced and reliable evaluation of output quality.

GPT-Based Evaluation supplements objective metrics like Ground Truth F1 Score in the assessment of model quality, contributing to a more comprehensive understanding of performance and informing reward allocation within the Proof-of-Quality (PoQ) system. Data indicates that both Llama-3.2-3B and Gemma-2-2B achieved an Average Ground Truth F1 Score of 5.35, suggesting a comparable level of accuracy as measured by token-level precision and recall. This score is used in conjunction with other metrics, such as latency, to determine the overall reward for inference and evaluator nodes, ensuring a balance between quality and cost-efficiency.

Experimental results indicate that the Llama-3.2-3B model achieved the highest reward score of 0.62 normalized units among all tested inference nodes. This performance is attributable to its combination of a high Ground Truth F1 Score – reflecting output accuracy – and a low Average Per Sample Latency of 1.1 seconds. The Cost-Aware Incentive Mechanism prioritized models that efficiently balanced quality and computational cost, resulting in Llama-3.2-3B receiving the highest reward within the evaluation framework.

Monte Carlo simulations reveal that average Path-of-Q (PoQ) rewards differ between inference and evaluator nodes when normalized.

Building Trust: Towards a Verifiable AI Infrastructure

The burgeoning field of Proof of Quality (PoQ) finds a remarkably robust foundation in blockchain technology, offering a pathway to trustless AI inference. Traditional AI systems often rely on centralized authorities to verify results, creating potential points of failure and censorship; however, a distributed ledger can immutably record the inputs, outputs, and verification processes of AI models. This ensures data integrity by creating an auditable trail, preventing malicious manipulation of results, and fostering greater transparency. Crucially, blockchain’s inherent decentralization allows for verification by a network of independent nodes, eliminating the need for a single trusted party and establishing confidence in the AI’s outputs – a paradigm shift toward verifiable and reliable artificial intelligence. The technology allows for the creation of tamper-proof records, guaranteeing that AI inferences are based on authentic data and haven’t been compromised, thereby building a cornerstone for trustworthy AI infrastructure.

The integration of blockchain technology with federated learning unlocks the potential for analyzing complex, varied computational networks. This synergy allows artificial intelligence models to be trained across a landscape of diverse hardware – from powerful server farms to individual edge devices – without centralized data storage. Federated learning distributes the training process, enabling each participant to learn from its local data while only sharing model updates, preserving privacy and reducing bandwidth demands. The blockchain then serves as a secure and transparent ledger for these updates, verifying their authenticity and ensuring fair contribution from all participants. This distributed approach not only enhances efficiency in resource allocation, optimizing the use of heterogeneous computing power, but also fosters a more resilient and scalable infrastructure for large language models, capable of adapting to evolving computational environments.

The architecture anticipates potential malicious behavior through a carefully constructed incentive mechanism designed to encourage truthful reporting and discourage adversarial strategies. This system doesn’t rely on simply detecting bad actors, but rather on rewarding honest participation and penalizing attempts to manipulate the network. By aligning the economic interests of all parties – those providing data, computational resources, and verification services – the framework creates a self-regulating ecosystem. Participants are incentivized to report accurate results and contribute genuinely, as doing so maximizes their rewards. Conversely, attempts to submit fraudulent data or compromise the integrity of the inference process are met with financial penalties, effectively mitigating the risk and fostering a robust, trustworthy environment for large language model operations.

Inference model performance reveals a trade-off between accuracy-measured by F1 score or GPT-based assessment-and speed, as higher accuracy generally corresponds to increased latency.

The Future of Verifiable AI and Decentralized Inference

Zero-Knowledge Machine Learning (ZKML) represents a significant advancement in the pursuit of both privacy and verifiability within Proof-of-Quality (PoQ) systems. This technology enables the validation of machine learning model outputs without requiring access to the underlying data or model parameters. Essentially, ZKML allows a verifier to confirm that a computation was performed correctly, and that the resulting output meets predefined criteria, all while keeping the sensitive inputs and internal workings of the model completely concealed. This is achieved through cryptographic techniques that generate proofs of correctness, which can be efficiently verified. By integrating ZKML into the PoQ framework, developers can build AI systems that offer a heightened level of security and trust, fostering broader adoption in applications where data privacy is paramount, such as healthcare, finance, and personalized services. The ability to verify AI outputs without revealing the inputs fundamentally shifts the paradigm, enabling a future where AI can be both powerful and protective of sensitive information.

Optimistic Machine Learning represents a paradigm shift in how AI systems are validated, operating on the principle that most computations will be honest and correct. Rather than exhaustively verifying every inference, OPML assumes integrity and only initiates a more computationally intensive verification process when a dispute arises – for instance, if different evaluators disagree on an outcome. This approach dramatically reduces overall computational overhead, as the vast majority of inferences proceed without costly checks. By leveraging this ‘optimistic’ assumption, OPML enables scalable and efficient decentralized inference, making verifiable AI more practical and accessible – a key factor in building trust and fostering innovation within AI ecosystems.

Recent advancements in verifiable artificial intelligence hinge on the development of robust evaluator models, and the STS-DistilRoBERTa architecture demonstrates a particularly strong performance in this capacity. Studies reveal this model achieves a correlation of 0.66 between its evaluations and the established ‘ground truth’ F1 score – a metric for a model’s precision – representing a substantial improvement over traditional cross-encoder methods. This heightened correlation indicates that STS-DistilRoBERTa can reliably assess the quality of AI outputs without requiring access to the original training data or model parameters, proving crucial for decentralized inference and fostering trust in AI systems where transparency and independent verification are paramount. The ability to accurately gauge performance with such efficiency opens doors to scalable and secure AI applications across various domains.

The convergence of Proof-of-Quality (PoQ), Zero-Knowledge Machine Learning (ZKML), and Optimistic Machine Learning (OPML) establishes a foundation for a decentralized and verifiable artificial intelligence ecosystem. This framework transcends traditional centralized models by distributing computational power and validation processes, fostering a more resilient and transparent AI landscape. The ability to verify AI outputs without revealing underlying data, coupled with an assumption of honest behavior punctuated by dispute resolution, dramatically reduces computational burdens and incentivizes participation. Consequently, this system not only promotes innovation by lowering barriers to entry but also cultivates trust amongst stakeholders, ensuring accountability and reliability in AI-driven applications and paving the way for broader adoption across diverse industries.

The pursuit of decentralized LLM inference, as detailed within, mirrors the inevitable entropy of any complex system. This research acknowledges that maintaining quality within a distributed network isn’t a static achievement, but a continuous negotiation with cost and efficiency. Paul Erdős observed, “A mathematician knows a lot of things, but not everything.” Similarly, this paper doesn’t claim a perfect solution, but a framework-a Proof of Quality-that adapts to the inherent trade-offs. The system’s longevity isn’t measured by initial perfection, but by its capacity to gracefully age, balancing the demands of quality and resource allocation as conditions evolve. It’s a testament to the fact that even the most innovative structures are subject to the passage of time and the need for constant recalibration.

What Lies Ahead?

The pursuit of decentralized large language model inference, as explored in this work, inevitably confronts the entropic realities of any distributed system. This proposed Proof of Quality mechanism, while demonstrating a viable path toward incentivizing both performance and efficiency, merely addresses the initial decay. The true challenge isn’t establishing quality control, but maintaining it over time. Every abstraction carries the weight of the past; the current formulation, predicated on specific cost and quality metrics, will require continuous recalibration as model architectures and computational landscapes evolve.

A critical, and often overlooked, aspect concerns evaluator reliability. The blockchain provides a robust record, but the data informing that record remains susceptible to manipulation or drift. Future work must address the meta-problem of evaluating the evaluators – a recursive task with diminishing returns, yet essential for preserving systemic integrity. Simply scaling the number of evaluators offers only temporary respite; the signal will inevitably be lost in the noise unless evaluation protocols themselves are subject to rigorous, longitudinal analysis.

Ultimately, the longevity of such a system hinges not on innovation, but on the deliberate embrace of slow change. The quest for optimal efficiency is a phantom; only resilience, built through incremental adaptation and a constant awareness of inherent limitations, will determine whether this approach ages gracefully, or succumbs to the inevitable pressures of a dynamic environment.

Original article: https://arxiv.org/pdf/2512.16317.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/