Overview
CheckerChain is a next-gen AI-powered crypto review platform that powers a trustless review consensus mechanism (tRCM). In tRCM protocol, anyone can participate but the protocol selects the reviewers arbitrarily to review a product. Selected reviewers can only get reward for their work when their review score falls in consensus range. Closer the consensus, more the reward.
Reviewers have a higher probability to make their review closer to consensus only when they are honest. Any dishonest review by any reviewer falls outside of consensus. This generates no or least reward making dishonest reviews highly expensive to perform. This will eventually discourage such attackers from participating in the tRCM protocol.
Subnet Introduction
CheckerChain subnet operates as a decentralized AI-powered prediction layer, continuously refining review scores through machine learning. It is structured into two key components: validators and miners. Validators play a crucial role in distributing product review tasks to miners and aggregating the Ground Truth ratings collected from the main platform. They evaluate miner-generated predictions, benchmarking them against the Ground Truth to ensure accuracy. By maintaining a competitive environment, validators score miners to optimize their models for better precision and efficiency.
Miners, on the other hand, are responsible for running AI models that predict review scores for listed products. These models evolve over time by learning from past predictions and adjusting their algorithms based on discrepancies with the Ground Truth. Through Reinforcement Learning from Human Feedback (RLHF), miners incorporate additional insights from validators and human reviewers, ensuring their models align more closely with real-world assessments. This continuous feedback loop allows the subnet to improve autonomously, reducing biases and increasing reliability in AI-driven ratings.
The subnet follows a decentralized learning and incentive structure, where AI models start with predefined datasets and historical review scores. Over time, miners fine-tune their models by comparing predictions with Ground Truth data, optimizing accuracy through RLHF. Validators play a key role in integrating tRCM-based human feedback into the training process, refining AI predictions iteratively. As a result, miners that consistently produce high-accuracy predictions receive higher benchmarks and greater rewards, creating a self-reinforcing cycle of improvement.
By combining tRCM human-decentralized ratings with AI-driven predictions, CheckerChain’s subnet evolves into a self-learning, decentralized, and transparent review system. The open participation model allows anyone to join as a miner or validator, contributing to an AI-enhanced ecosystem that continuously adapts to real-world opinions. This fusion of human intelligence and AI automation ensures a fair, scalable, and corruption-resistant review platform, setting a new standard for decentralized trust in product evaluations.