PoQ-Judge: A Multi-Architecture Evaluation Framework for Cost-Aware Proof-of-Quality in Decentralized LLM Inference
arXiv:2606.11196v1 Announce Type: new Abstract: Decentralized LLM inference networks need lightweight, reference-free quality evaluation for Proof of Quality (PoQ). We present PoQ-Judge, a framework that trains dedicated judge models to score query-output pairs without ground-truth references. We study three architectures across the quality-cost tradeoff: a TextCNN judge, a MiniLM cross-encoder, and a DeBERTa judge. Using two-stage training on UltraFeedback plus GPT-labeled in-domain data, the best model reaches 0.747 Pearson correlation with the ground-truth proxy on a held-out test set, outp
The proliferation of decentralized LLM inference networks creates an immediate need for robust, cost-effective, and reference-free quality evaluation methods.
Reliable quality assessment is critical for the economic viability and trustworthiness of decentralized AI, enabling proper compensation and preventing the propagation of low-quality outputs.
The ability to accurately and efficiently evaluate LLM outputs in decentralized environments reduces reliance on centralized authority or prohibitively expensive human labeling.
- · Decentralized AI inference providers
- · LLM developers seeking cost-effective quality assurance
- · Blockchain infrastructure for AI
- · AI fairness and transparency researchers
- · Centralized LLM inference platforms reliant on manual evaluation
- · Cloud providers with high inference costs
- · Outmoded qualitative evaluation methods
More efficient and reliable decentralized LLM inference becomes possible due to automated quality assurance.
This could accelerate the adoption of peer-to-peer AI services and reduce entry barriers for smaller AI models.
A robust 'Proof of Quality' mechanism might lead to new economic models for AI compute, potentially disrupting traditional cloud service offerings.
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Read at arXiv cs.CL