Academic Research Support

The design logic, technical feasibility, and performance advantages of the aforementioned PoQ mechanism have been validated through dedicated academic research. For detailed insights, refer to the following papers:

1. Design and Evaluation of Cost-Aware PoQ for Decentralized LLM Inference: Proposes a cost-aware PoQ framework that integrates efficiency metrics into incentive mechanisms. It achieves dynamic balance between quality and cost through a unified evaluation pipeline, verifying the practicality and economic sustainability of the multi-dimensional evaluation system.
2. Optimistic TEE-Rollups: A Hybrid Architecture for Scalable and Verifiable Generative AI Inference on Blockchain: Presents a hybrid verification protocol that provides the underlying architecture for on-chain verifiable proofs. By combining Trusted Execution Environments (TEE) with zero-knowledge spot checks, it resolves the trilemma of verifiability in decentralized inference and further enhances the security and efficiency of PoQ proofs.
3. Adaptive and Robust Cost-Aware Proof of Quality for Decentralized LLM Inference Networks: Extends the cost-aware PoQ mechanism with adversary-resilient consensus formation, integrating robust aggregation rules (median, trimmed mean) and an adaptive trust-weighted consensus strategy that updates evaluator weights based on deviation signals. Through experiments on question answering and summarization tasks with four adversarial attack strategies, it verifies that robust aggregation improves the alignment of consensus scores with ground truth proxies and reduces sensitivity to noisy and strategic attacks, while also clarifying the operational trade-offs between evaluator sampling size, robustness, reward variance and evaluation overhead, providing practical guidance for PoQ deployment under adversarial risks and resource constraints.