Whitepaper v2.0

CheckerChain: Next-Gen AI-powered Crypto Review Platform

ABSTRACT. Existing online review platforms are not able to solve the problem of manipulated and fake reviews. Traditional centralized review platforms do not even incentivize users for their contributions. We believe these problems are due to the lack of a consensus mechanism in review architecture. Utilizing blockchain technology will partially solve the need of transparent and tamper-proof reviews.

In this paper, we propose an AI-powered trustless review consensus mechanism (tRCM) on top of blockchain technology. This tRCM architecture enables reviewers to reach a consensus on an opinion without the need of knowing each other's opinions and completely removes the chance of fake or manipulated reviews to receive any incentives.

PREFACE

This protocol is experimental; can suffer from parameter changes and logical upgrades. Improved methodologies can be integrated upon rigorous revisions from both the core team and the community. This paper outlines the most up-to-date version of this protocol which is intended to be completely open-source to develop the desired review aggregator platform.

2. INTRODUCTION

There are hundreds of thousands of crypto projects, products, companies, books, movies, and events happening every day. It is a real hassle to find the best product when we have to trust the feedback and reviews from unknown or known parties without any consensus mechanism. The existing review system is completely broken and unfair as there is no trustless check system to avoid paid, promoted, or faked reviews.

Company name

Per year revenue

Monthly user

Market Share

TripAdvisor

604m USD

160m

TrustPilot

102m USD

57.3m

1.5%

ProductHunt

3.3m USD

6.23m

Better Business Bureau

215m USD

13.83m

Yelp

872.9m USD

142.5m

Google

282b USD

88.33b

73%

Facebook

85.96b USD

18.13b

We introduce a decentralized and trustless review protocol built on blockchain where opinion-checkings are incentivized on reaching a consensus. Millions of these users can get revenue shared with CheckerChain. Posters, Reviewers, and Influencers are rewarded based on the quality of their work with $CRCN token.

CheckerChain is a next-gen AI-powered crypto review platform built on blockchain using trustless review consensus mechanism (tRCM). Anyone can become a "Poster" to publicly list any crypto products and anyone can nominate themselves in tRCM protocol to participate in reviewing process. Reviewers are arbitrarily selected from the nominee pool by the protocol in zero-knowledge conditions. While the tRCM protocol is utilized for reviewing only crypto related products in CheckerChain platform, there exists a "tRCM-as-a-Service (tAAS)" extension to implement tRCM across multiple categories such as movies, books, electronics, cities, restaurants, hotels, and more.

3. INTERACTION ON CHECKERCHAIN

CheckerChain uses tRCM protocol, an evolutionary upgrade of the existing review industry with a decentralized philosophy. Hence, our protocol is built on blockchain, which is the most decentralized, the most secure and the most valueable technology network.

CheckerChain operates with 3 user types:

(a) Posters: users who list crypto products on CheckerChain (b) Reviewers: users who are nominated by tRCM protocol to write reviews (c) Influencers: users who interact, boost, and engage on CheckerChain

4. tRCM ARCHITECTURE

tRCM is an acronym for trustless Review Consensus Mechanism. It is the core protocol utilized on CheckerChain to make reviews trustless.

tRCM is based on 2 assumptions for a review to hold any authentic value,

reviews are performed in zero-knowledge proofs without any control of either the poster or the reviewer.
honest reviewers in the protocol always establish a majority

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.

These scores are vital parameters to derive incentives for each contribution.

Trust Score: This is an atomic data of a product calculated from reviewer's task. It represents rating of a product in the range of 0 to 100.
Normalized Trust Score: This is a derived data of a product calculated from Trust score to determine the impact on reward. Posters receive reward based on Normalized Trust score.
Consensus Score: This is an atomic data of reviewer's task. It represents the quality of trust score in the range of 0 to 100.
Profile Score: This is both an atomic and aggregated data of reviewer's performance. It represents the quality of consensus in the range of 0 to 100. Reviewers receive reward based on Profile score.
Rating Score: This is a derived data of a product calculated as Trust score out of 5 and processed with Bayesian Average.
Feedback Score: This is an aggregated data of reviewer's task combined with influencer's task. It represents sentiments of a product in the range of 0 to 5.
Normalized Feedback Score: This is a derived data calculated from Feedback Score; processed with Bayesian Average.
Sentiment Score: This is a categorical data of a product from reviewer's task. It is non-numerical in category and numerical inside a category in the range of 0 to 100.
Ranking Score: This is a derived data of a product calculated from Rating score and Consensus score. It represents the rank of a product starting from 1 over 24h, 7 days, 30 days and All-Time ranges.

4.1 Trustless Review Process

When a product gets listed on CheckerChain, tRCM protocol enacts on 30+ parameters of 10 categories to generate 3 vital atomic scores: Trust Score of Product, Profile Score of Reviewer and Consensus Score of Review Cycle.

10 Categorical Metrics of CheckerChain for Crypto Reviews

Let $Ri=(ri1,ri2,...,ri30)$ represent the assessment of the $𝑖$ -th randomly assigned reviewer across the 30 parameters. Each $𝑟𝑖𝑗$ denotes the assessment of the $𝑗$ -th parameter by the $𝑖$ -th reviewer.

At the end of the assessment time limit, a consensus $C=(c1,c2,...,c30)$ is derived based on the submitted values of all reviewers. Each $𝑐𝑗$ represents the consensus value for the $𝑗$ -th parameter.

To quantify how closely each reviewer's assessment aligns with the consensus, a similarity measure $sim(𝑅𝑖,𝐶)$ is introduced. This measure quantifies the degree of agreement between the assessment of the $𝑖$ -th reviewer and the consensus opinion. Similarity metrics such as cosine similarity and Euclidean distance have been employed for this purpose. A higher similarity indicates a stronger alignment with the consensus and thus a higher Trust Score.

A function $𝑓(sim(𝑅𝑖,𝐶))$ is utilized to map the similarity for a Trust Score within the range of 0 to 100, and involves scaling, normalization, and mathematical transformations.

$TSi=f(sim(Ri,C)),∀i=1,2,...,N$

Where:

$𝑇𝑆𝑖$ is the Trust Score assigned to the $𝑖i$ -th reviewer's assessment.
$𝑁$ is the total number of randomly assigned reviewers.
$𝑅𝑖$ represents the assessment of the $𝑖$ -th reviewer.
$𝐶$ represents the consensus derived from all reviewers' assessments.
$sim(𝑅𝑖,𝐶)$ is the similarity measure between the $𝑖$ -th reviewer's assessment and the consensus.

If any reviewer disagrees with the Trust Score assigned in the genesis round, indicating a lack of consensus among reviewers, another round of assessment is initiated. In this subsequent round, a new set of randomly assigned reviewers evaluates the product, and the process repeats until a satisfactory level of consensus is reached.

Given a reviewer's assessment vector $𝑅𝑖$ and the consensus vector $𝐶$ , the Profile Score $𝑃𝑆𝑖$ for the $𝑖$ -th reviewer can be calculated as:

$PSi=1/[1+euclidean(Ri,C)]×100$

This scales the inverse of the Euclidean distance to a range of 0 to 100, where higher values indicate closer alignment with the consensus.

We define $penaltymissi(t)$ as the penalty for missing tasks and $penaltyaligni(t)$ as the penalty for not aligning with the consensus for the $𝑖$ -th reviewer at time $𝑡$ within an epoch. These two factors impact the profile scores within an epoch and are reset on a new epoch.

4.2 Blockchain Infrastructure

CheckerChain uses multiple blockchain infrastructures to store all review outputs in a decentralized, immutable and tamper-proof system. Built on blockchain, CheckerChain provides its users an absolute transparency and complete ownership.

Inscription of Review Data (IRD) on Bitcoin

When a consensus is generated from tRCM for any product, it incorporates the review data for that cycle into the OP_RETURN field of SegWit-enabled transactions. With SegWit, the witness data, including the digital signature, is stored separately from the transaction data, resulting in a more compact representation of the review data within the OP_RETURN field. Furthermore, Taproot enhances privacy by obfuscating the spending conditions associated with the transaction, thereby safeguarding the confidentiality of review-related activities.

The process begins with encoding the review data into a format suitable for storage in the OP_RETURN field. This involves converting the structured data into a hexadecimal representation to ensure compatibility with the Bitcoin protocol.

import hashlib
import bitcoin
from bitcoin.wallet import CBitcoinAddress, CBitcoinSecret
from bitcoin.core import CTransaction, COutPoint, CTxOut, CMutableTxOut, CMutableTransaction, CScript, OP_RETURN

def serialize_review_data(data):
    # Serialize review data into a structured format
    serialized_data = serialize_to_json(data)
    return serialized_data

def construct_transaction(serialized_data, address):
    # Construct Bitcoin transaction
    txout = CMutableTxOut(0, CScript([OP_RETURN, serialized_data]))  # Embed serialized data in OP_RETURN
    tx = CMutableTransaction([txout])
    tx_hex = tx.serialize().hex()
    return tx_hex

def broadcast_transaction(tx_hex):
    # Broadcast transaction to Bitcoin network
    try:
        bitcoin.pushtx(tx_hex)
        print("Transaction broadcasted successfully.")
    except Exception as e:
        print(f"Error broadcasting transaction: {e}")

# Example usage
review_data = {
    "product_id": "Bitcoin",
    "trust_score": 99,
    "consensus": 93,
    "ratings": 4.8
}

serialized_data = serialize_review_data(review_data)
tx_hex = construct_transaction(serialized_data, "1BitcoinAddress")
broadcast_transaction(tx_hex)

Next, a new Bitcoin transaction is constructed with a custom scriptPubKey containing the OP_RETURN opcode followed by the encoded review data. This scriptPubKey specifies the conditions under which the funds can be spent, in this case, marking the output as unspendable and designating it for data storage.

Once the transaction is constructed, it is broadcast to the Bitcoin network and included in a block by miners. The review data becomes permanently recorded on the blockchain, providing an immutable and publicly accessible record.

To retrieve the review data of any product, users can query the Bitcoin blockchain using a Bitcoin node or blockchain explorer. The encoded data is then decoded from its hexadecimal representation, allowing for the review information to be accessed and analyzed.

Tokenized Asset: $CRCN

CheckerChain uses $CRCN tickered digital assets for rewarding all contributors in review-to-earn model. $CRCN is etched and managed using protocols like Runes on Bitcoin.

Runes protocol utilizes Bitcoin’s UTXO (Unspent Transaction Output) model. Every Bitcoin transaction involves consuming existing UTXOs as inputs and then creating new UTXOs as outputs. Runes protocol aims to make the creation and management of fungible tokens on Bitcoin much more streamlined. Using Runes, CheckerChain embeds $CRCN within a UTXO, marking it as a Rune and assigning specific properties to it.

Each $CRCN is identified by a Rune ID that references the block and transaction where it was created, allowing easy tracking and proof of origin.

Inscription of Review Data (IRD) on Ethereum, EVM-compatible Chains

The Ethereum blockchain, along with other EVM-compatible chains such as Binance Smart Chain, Polygon, and Avalanche C-Chain, provides a flexible framework for implementing CheckerChain’s Inscription of Review Data (IRD). The process begins with the serialization of review data—including product identifiers, trust scores, consensus metrics, and user ratings—into a structured JSON format. To ensure compatibility with the transaction structures of these networks, this serialized data is subsequently encoded into a hexadecimal representation.

pythonCopy codeimport json

def serialize_review_data(data):
    """
    Serializes review data into a JSON string.
    """
    return json.dumps(data)

review_data = {
    "product_id": "CheckerChain",
    "trust_score": 95,
    "consensus": 92,
    "ratings": 4.7
}
serialized_data = serialize_review_data(review_data)
print(f"Serialized Data: {serialized_data}")

CheckerChain employs a purpose-built smart contract to facilitate the storage, retrieval, and management of review data. This contract not only enables the immutable storage of data but also automates the distribution of $CRCN tokens under the platform's review-to-earn incentive model. When a transaction is initiated, the encoded review data is embedded in the transaction’s payload and transmitted to the smart contract. Upon successful execution, the smart contract inscribes the review data onto the blockchain, thereby establishing a decentralized and publicly verifiable record.

solidityCopy code// Solidity: Sample Smart Contract for CheckerChain
pragma solidity ^0.8.0;

contract CheckerChain {
    mapping(bytes32 => string) public reviewData;

    function storeReviewData(bytes32 reviewId, string calldata data) external {
        reviewData[reviewId] = data;
    }

    function getReviewData(bytes32 reviewId) external view returns (string memory) {
        return reviewData[reviewId];
    }
}

Contributors to the review process are rewarded with $CRCN tokens, which are implemented as ERC-20 standard assets to ensure compatibility and fungibility. The design of these tokens integrates principles similar to the Runes protocol on Bitcoin, enabling efficient tracking and streamlined token management. For data retrieval, users can query the smart contract through blockchain explorers, such as Etherscan, or via dedicated APIs. The retrieved data is then decoded from its hexadecimal format into its original structure for verification and analysis.

pythonCopy codefrom web3 import Web3

def retrieve_review_data(contract_address, review_id):
    """
    Retrieves review data from the smart contract.
    """
    contract = web3.eth.contract(address=contract_address, abi=contract_abi)
    review_data = contract.functions.getReviewData(review_id).call()
    return review_data

By leveraging the programmability and widespread tooling support of Ethereum and EVM-compatible networks, CheckerChain ensures a robust, transparent, and efficient implementation of its IRD framework.

Inscription of Review Data (IRD) on Non-EVM Compatible Chains

Non-EVM-compatible blockchains, such as Solana, Cardano, and Polkadot, offer distinct architectural paradigms that CheckerChain adeptly integrates into its IRD framework. On the Solana blockchain, review data is first serialized into a JSON format and then encoded into a compact hexadecimal representation. Solana’s account-based model facilitates efficient data storage by associating a dedicated blockchain account with each review cycle or product. A custom Solana program, analogous to a smart contract, manages the validation and inscription of review data into these accounts.

rustCopy code// Solana Program: Store Review Data
use solana_program::{
    account_info::{next_account_info, AccountInfo},
    entrypoint,
    pubkey::Pubkey,
    program_error::ProgramError,
    msg,
};

entrypoint!(process_instruction);
fn process_instruction(
    program_id: &Pubkey,
    accounts: &[AccountInfo],
    input: &[u8],
) -> ProgramResult {
    let account = next_account_info(accounts)?;
    account.data.borrow_mut().copy_from_slice(input);
    msg!("Review data stored successfully.");
    Ok(())
}

On Cardano, the review data is inscribed as transaction metadata within its extended UTXO (eUTXO) model. The serialized metadata, embedded within the transaction structure, is stored on-chain, ensuring low-cost and high-throughput operations. Data validation and reward distribution are orchestrated through Plutus scripts, Cardano’s smart contract framework.

haskellCopy code-- Cardano: Plutus Script Example
{-# LANGUAGE DataKinds #-}
{-# LANGUAGE NoImplicitPrelude #-}
{-# LANGUAGE TemplateHaskell #-}
{-# LANGUAGE ScopedTypeVariables #-}

module CheckerChain where

import PlutusTx
import PlutusTx.Prelude
import Ledger
import Ledger.Typed.Scripts
import Prelude (String)

{-# INLINABLE storeReviewData #-}
storeReviewData :: String -> BuiltinData -> Bool
storeReviewData review _ = traceIfFalse "Review data validation failed." (length review > 0)

In the Polkadot ecosystem, CheckerChain utilizes parachains such as Moonbeam and Astar, each tailored to specific operational needs. On Moonbeam, which supports EVM compatibility, smart contracts are deployed to store and manage review data. Conversely, on WASM-based parachains such as Astar, the data is inscribed using native chain records.

By tailoring its multichain framework to the specific features of each blockchain, CheckerChain ensures the secure and immutable inscription of review data while maintaining universal accessibility. The integration of $CRCN tokens across these platforms serves as a unifying reward mechanism, enhancing interoperability and trust within the ecosystem.

4.3 Artificial Intelligence (AI)

In tRCM protcol of CheckerChain, BERT and LSTM are implemented to fine-tune all 10 categories and their 30 parameters over the training models. Each review $𝑅𝑖$ is represented as a sequence of review data, denoted by $𝑅𝑖=(𝑤𝑖1,𝑤𝑖2,...,𝑤𝑖𝑛)$ , where $𝑛$ is the number of reviews in the cycle. BERT extracts contextualized embeddings for each value in the review, resulting in embeddings $𝐸𝑖𝑗$ for the $𝑗$ -th token in the $𝑖$ -th review. Mathematically, this can be represented as:

$𝐸𝑖𝑗=BERT(𝑤𝑖𝑗)$

These embeddings are then fed into an LSTM network for sequence processing, producing hidden states $𝐻𝑖$ for each review. The LSTM process can be represented as:

$𝐻𝑖=LSTM(𝐸𝑖1,𝐸𝑖2,...,𝐸𝑖𝑛)$

Once the hidden states $𝐻𝑖$ are obtained, they are analyzed to derive sentiment scores $𝑆𝑖𝑘$ and assessment scores $𝐴𝑖𝑚$ for each category $𝐶𝑘$ and parameter $𝑃𝑚$ respectively. Mathematically:

$𝑆𝑖𝑘=SentimentAnalysis(𝐻𝑖,𝐶𝑘)$

$𝐴𝑖𝑚=AssessmentAnalysis(𝐻𝑖,𝑃𝑚)$

In the context of trustless reviews, each reviewer performs zero-knowledge proof, ensuring the confidentiality of reviewers' identities and assessments. Mathematically, this process can be denoted as $ZeroKnowledgeProof(𝑅𝑖)$ , where the review $𝑅𝑖$ is transformed into a zero-knowledge proof representation.

By integrating these mathematical models into the trustless review system, CheckerChain ensures both the privacy and accuracy of tRCM outputs while leveraging advanced AI techniques for fine-tuning the review process.

4.4 Subnet AI Infrastructure With Miner & Validator

CheckerChain's subnet infrastructure introduces a decentralized and performance-driven review mechanism. Validators play a pivotal role by distributing review tasks to miners, who deploy and finetune their AI models to predict review scores for listed products.

After each review cycle of a product, miners are evaluated on the accuracy of their predictions relative to the final review score from the tRCM. Validators benchmark miners accordingly, ensuring a competitive environment that encourages precision and efficiency in AI modeling. Performance-based rewards are distributed to both miners and validators, aligning the subnet's operation with CheckerChain's broader objectives of decentralization, transparency, and robust review data analysis.

Anyone can join to run a miner or validator on the CheckerChain subnet.

4.5 Decentralized Storage of Review Data & AI Agent Benchmarking

CheckerChain utilizes 0G’s decentralized storage infrastructure to manage its review data, ensuring cost efficiency, data integrity, and accessibility within a decentralized framework. The adoption of 0G’s solution provides significant cost advantages, reducing storage expenses by up to 80% compared to traditional cloud services like Amazon S3. Beyond cost-effectiveness, 0G’s decentralized network ensures data provenance by maintaining the authenticity and integrity of stored reviews, while its censorship-resistant architecture safeguards the data against tampering or unauthorized alterations. This combination of attributes underscores CheckerChain’s commitment to secure, reliable, and transparent data management practices.

The integration of 0G’s data availability layer enhances CheckerChain’s ability to support real-time querying and data-driven applications. The highly accessible review data enables AI agents and users to retrieve and analyze structured information with efficiency, facilitating timely decision-making processes. Moreover, this infrastructure serves as a robust foundation for training and refining AI models, enabling them to improve in predicting review scores and conducting sentiment analysis. By ensuring consistent and reliable data access, CheckerChain positions itself as an advanced platform for real-time analytics and informed evaluations.

CheckerChain with 0G extends beyond storage and accessibility to strategic integration within 0G’s AI agent ecosystem and marketplace. This lays the groundwork for innovative applications such as “agent breeding,” where high-performing AI agents are combined to create new, optimized iterations, further advancing the ecosystem’s technological capabilities.

5. PROTOCOL INCENTIVES

CheckerChain protocol incentivizes all contributions made by anyone within the ecosystem. When the majority of participants are honest, the trustless review consensus mechanism (tRCM) becomes secure and realistic. This protocol is designed to distribute incentives based on the consensus level of tRCM achieved.

Initially, for 8 years, incentives are maintained by distributing the new tokens. When 250,000,000 $CRCN tokens get completely distributed at the end of 8 years, the inflation rate drops to 0%, and incentives are maintained by distributing revenues earned by the platform.

Protocol incentives are distributed on a monthly basis and counted as Epoch. The contributions of posters, reviewers and influencers are collectively accounted per epoch into performance scores. Based on these monthly scores, rewards are emitted by the Distributor Smart Contract. All scores get reset every epoch.

Any contributions on CheckerChain platform are incentivized through its own digital assets called $CRCN.

6. UTILITIES & ECOSYSTEM

CheckerChain is a utility-driven decentralized application (dApp). It fixes the problem of fake and manipulated review systems and is also designed with a gamified review-to-earn ecosystem.

6.1 Trustless Review Platform

CheckerChain is a next-gen AI-powered trustless review platform. This is one of the most innovative evolutions in the review industry. While CheckerChain is particularly focused on the crypto and blockchain segment, it is a much-needed upgrade that can be experimented with in all other categories where reviews are important.

6.2 Review-to-Earn Platform

Traditional review platforms have no mechanism to incentivize contributors without impacting review metrics. CheckerChain integrates tRCM bringing fairness to review metrics while also incentivizing all contributors.

6.3 tRCM as a Service (TaaS) for third-parties

tRCM is a revolutionary protocol. It does not need to be limited within a platform. Hence, tRCM as a Service (TaaS) model is available for any third parties to utilize tRCM. It can extend from a crypto review platform to multiple segments such as movies, books, electronics, cities, restaurants, hotels, and more.

6.4 Revenue Sharing

Posters, reviewers, and influencers are automatically rewarded. As the platform grows to generate revenues, participants in this ecosystem can act as beneficiaries.

6.5 Asset Utility (Payment for Services, Staking, Liquidity-Providing)

$CRCN tokens can be utilized as payment for various services where tRCM is implemented. Holders can earn rewards for staking or providing liquidity.

6.6 Gamified UX

CheckerChain is an interactive web3 platform where contributions are incentivized and boosted through a gamified model. UI and UX are designed to support various achievements, badges, digital artifacts, and leaderboards.

7. TOKENOMICS

$CRCN token is a native digital asset. The maximum supply of $CRCN token is 2,100,000,000 and it has 5 decimals.

Distribution

Amount ($CRCN)

Allocation %

Early Investors

58,000,000

2.76%

Reviewers Incentives

150,000,000

7.14%

Posters Incentives

50,000,000

2.38%

Influencers Incentives

50,000,000

2.38%

Staking Incentives

75,000,000

3.57%

LP Incentives

175,000,000

8.33%

Market Making DEX/CEX

146,000,000

6.95%

Bridges to DEX/CEX

150,000,000

7.14%

Business Development

250,000,000

11.90%

Market Development

346,000,000

16.48%

Investors/VCs

200,000,000

9.52%

Team Development

100,000,000

4.76%

Advisors Incentives

100,000,000

4.76%

Foundation Reserve

250,000,000

11.90%

TOTAL

2,100,000,000

100%

7.1 Distribution scheduled within 8 years are Reviewers Incentives, Posters Incentives, Influencers Incentives, Advisors Incentives, Team Development, LP Incentives, and Staking Incentives. The distribution curve of 8 years is maintained at 18%, 17%, 16%, 14%, 12%, 10%, 8%, and 5% in chronological order.

Time Period

8-year Distribution

Example: Poster Incentives

1st year period

18%

9,000,000

2nd year period

17%

8,500,000

3rd year period

16%

8,000,000

4th year period

14%

7,000,000

5th year period

12%

6,000,000

6th year period

10%

5,000,000

7th year period

4,000,000

8th year period

2,500,000

7.2 Revenue allocations:

Allocations

8-year Distribution

After 8-year

Posters

10%

Reviewer

30%

Influencers

10%

Stakers

10%

Liquidity Providers

10%

Leaderboard

00%

10%

Foundation

100%

20%

7.3 Reward Distribution:

Method

Calculation Basis

Period

Distribution

Poster

Trust Score

1st of Month