Litepaper

1. Introduction

Why?

The rapid advancement of artificial intelligence (AI) has positioned it as a transformative force across industries, revolutionizing decision-making, automation, and knowledge creation. However, the existing AI landscape remains deeply centralized, dominated by a few monopolistic entities that control data, computing power, and model access. Only a handful of super companies have mastered large-scale model training and inference technologies.

Many challenges faced by large models, including hallucinations, stem from data scarcity. The supply of low-cost, publicly available internet data is nearing exhaustion, driving up the cost of data acquisition. Further challenges include the limited availability of personal and high-quality industry data, the difficulty of leveraging such data at scale while preserving privacy, the complexity of assessing data quality and effectiveness, and the lack of mechanisms for individuals and enterprises to receive fair compensation for their data. Going forward, the success of AI applications will largely hinge on how effectively data is generated and utilized.

In summary, LazAI seeks to address the following key challenges:

1. Data Sharing for AI utilization is Challenging: In the AI domain, data is a foundational asset; however, the sharing of personal and industry-specific data remains significantly constrained due to strong privacy and security concerns. Both individuals and organizations are often reluctant to share data for fear of misuse, unauthorized access, or potential fraud. Moreover, the absence of standardized protocols and robust AI infrastructure further hampers the efficient sharing and utilization of data within AI workflows, even when stakeholders are willing to collaborate.

2. Data Quality and Evaluation Are Challenging: The quality of publicly available data is highly inconsistent, and a large proportion of high-quality data is proprietary or protected by copyright, limiting access for developers aiming to train competitive AI models. Establishing a unified framework to evaluate data effectiveness across diverse scenarios and perspectives is inherently difficult. Furthermore, there is a lack of viable mechanisms to support personalized, utility-based data evaluation, thereby restricting data optimization and impeding the overall progress of AI systems.

3. Revenue Generation and Distribution is Difficult: Throughout the lifecycle of AI model development and deployment, data contributors and model developers struggle to obtain fair compensation due to insufficient transparency and lack of verifiability. Centralized AI platforms often function as opaque systems, making it difficult to trace how data is utilized and how its value is realized. Consequently, data owners lack the ability to assess their data’s contribution to model outcomes or to claim appropriate economic rewards.

Therefore, we aim to build a world where everyone has the opportunity to align AI with their own data, build personalized AI models at minimal cost, and share in the value generated by their data and models through alignment. To achieve this, Lazai is committed to delivering decentralized AI blockchain infrastructure, AI asset protocols, and workflow toolkits. By leveraging decentralized, user-owned data sources, Lazai empowers developers to build value-aligned AI agents.

How?

In the era of artificial intelligence, data is the new oil—but its flow remains obstructed. Privacy concerns, fragmented infrastructure, and opaque value attribution mechanisms have long discouraged individuals and enterprises from contributing their data to AI systems. LazAI reimagines this paradigm with a simple yet transformative proposition: data should be shareable without compromise, transparently evaluated, and fairly rewarded.

At the core of LazAI is a fully on-chain, privacy-preserving AI runtime and governance framework. This infrastructure transforms the raw data contributed by each iDAO into verifiable, ownable, and rewardable digital assets. Whether it is personal insights or proprietary enterprise datasets, contributors can share their data with confidence, safeguarded by advanced cryptographic technologies such as Trusted Execution Environments (TEEs) and Zero-Knowledge Proofs (ZKPs). The contributed data is then validated through verified computing and finalized via QBFT consensus, achieving a consistent and trusted state on the LazAI chain. Every phase, from data contribution and model interaction to final settlement, is executed and governed transparently on-chain, eliminating black-box ambiguity and reestablishing trust in collaborative, decentralized AI.

But LazAI goes beyond enabling secure data sharing, it redefines how the value of data is understood and realized. In today’s landscape, inconsistent data quality and the absence of robust evaluation mechanisms make it nearly impossible to assess the true utility of a dataset in AI training. LazAI addresses this challenge by seamlessly integrating data alignment, governance, and AI model training/inference within a unified, verifiable pipeline. Upon data contribution to the LazAI chain, users receive a DAT token corresponding to the model that encodes ownership, traceability, and value attribution. These tokens empower contributors to track exactly how their data and models are used and to visualize impact through on-chain runtime metrics defined in DAT. This supports both standardized benchmarking and personalized, utility-based insights, enabling more strategic contributions and higher-performance AI systems.

Just as importantly, LazAI ensures that value never disappears into the system. As models consume data and generate outputs, contributors are rewarded in real time, with all revenue flows deterministically and transparently distributed on-chain, tied directly to verified usage. This closed-loop economic model defined within LazAI eliminates the need for intermediaries and dismantles the opacity of centralized platforms. Data contributors and model developers alike receive fair, auditable compensation, grounded in verifiable activity, not speculative markets. Through iDAO governance and establishing decentralized autonomous organizations (iDAOs) for domain-specific data curation, each iDAO enforces community-driven rules for data quality, access, and usage, fostering trust among participants.

By aligning privacy, utility, and economic rewards into a single interoperable framework, LazAI does more than solve the data challenges of AI, it unlocks a new paradigm. One where anyone can contribute, monitor, and benefit from the value they help create. One where AI innovation is no longer the privilege of the few with the most data, but the shared opportunity of a truly decentralized future.

LazAI envisions a future where the three foundational pillars of artificial intelligence, data, models, and compute power, are seamlessly and trustfully integrated on-chain through open and unified protocols. This architecture enables a transparent, decentralized, and composable AI ecosystem, built not on blind trust, but on provable integrity and incentive alignment.

2. LazAI: Building the Next-Generation AI Ecosystem

The future of AI requires an ecosystem that is open, composable, and trust-driven. However, today’s AI landscape is plagued by centralized control, data monopolization, and opaque decision-making, limiting the participation of independent developers and restricting access to critical AI resources.

LazAI pioneers a decentralized AI network that challenges the status quo by integrating blockchain technology, verifiable AI computing, and tokenized AI assets to create a transparent, scalable, and incentive-driven ecosystem. This approach not only democratizes AI access but also establishes a self-sustaining AI economy, where data and model developers, and infrastructure contributors are fairly rewarded for their participation.

LazAI provides blockchain infrastructure, protocols, and workflows based on iDAO's decentralized data sources to help developers build value-consistent AI agents. It also addresses the challenges of data sharing, quality assessment, and fair revenue distribution by integrating privacy protection technologies, verifiable computing, and other technologies.

1. Overcoming Data Sharing Challenges - To address the barriers of privacy concerns and fragmented data ecosystems, LazAI establishes a decentralized, encrypted network for secure data sharing. By integrating TEEs and ZKPs, the platform enables contributors to share encrypted data snippets without exposing raw information. Smart contracts govern all interactions on the LazAI chain, ensuring that data usage adheres to predefined privacy policies and access controls set by iDAOs. These iDAOs act as collaborative hubs for industry-specific data curation, fostering trust through transparent governance while preventing unauthorized access or misuse. The result is a permissioned yet open ecosystem where sensitive data (e.g., from personal health records to proprietary industrial datasets) can be safely utilized for AI training, inference and evaluation, unlocking value without compromising ownership.

2. Solving Data Quality and Evaluation Challenges - LazAI tackles the inconsistency of public data and the lack of unified evaluation frameworks through its DAT protocol. When data is contributed to the chain, it undergoes rigorous validation via TEEs and QBFT consensus, after which a DAT token is minted to represent its ownership and quality. Each token embeds metadata, usage history, and dynamic performance metrics such as how a dataset is used to improve model accuracy in natural language processing tasks. By transforming abstract data quality into quantifiable, tradable assets, LazAI creates a marketplace where high-value datasets command premium access and rewards, driving continuous optimization of AI models.

3. Enabling Fair Revenue Generation and Distribution - LazAI disrupts the centralized control of AI revenue by introducing a decentralized, real-time reward mechanism. Smart contracts automatically track data usage across model training, inference, and evaluation, distributing proceeds directly to contributors based on verifiable metrics such as the proportion of their data in a model’s training set or its contribution to inference outcomes. All transactions are recorded on-chain, providing immutable proof of data lineage and value attribution, while cross-chain interoperability enables seamless conversion of rewards into mainstream cryptocurrencies. This closed-loop economy eliminates intermediaries, ensures fair compensation for data contributors and model developers alike, and aligns incentives with verifiable impact, not speculative market forces.

Specifically, LazAI focuses on three fundamental pillars that drive the development of an autonomous, scalable, and composable AI ecosystem:

1. Trustless Privacy Data and Model Provenance – Establishing verifiable data integrity and seamless toolchain interoperability to break data silos and enable secure AI workflows.

2. Decentralized AI Execution and Incentive Framework – Enhancing AI efficiency by reducing computational costs, optimizing AI liquidity, and supporting scalable on-chain inference models. Through unified on-chain decentralized AI protocol and process (such as DAT protocol, on-chain model data training and inference metrics) to ensure fairness and openness.

3. Composability-Driven AI Economy – Creating a modular AI asset framework where datasets, models, and AI applications are tokenized, tradable, and seamlessly composable.

2.1 Trustless Privacy Data and Model Provenance

AI is only as good as the data it learns from. However, centralized AI platforms often suffer from closed data silos, unverifiable data origins, and fragmented toolchains, making it difficult for developers to build high-quality, explainable AI models.

LazAI’s Solution: Verifiable AI Data & Composable Toolchains

LazAI introduces a trustless AI validation framework that ensures every dataset, model, and AI computation is verifiable, auditable, and immutable:

1. iDAO-Powered AI Governance: AI datasets and models are governed by Quorum-Based Consensus, ensuring decentralized validation of data sources and AI workflows.

2. Data Anchoring Token (DAT): AI assets are tokenized and recorded on-chain, allowing transparent ownership, verifiable provenance, and permission-based access control.

3. POV (Point of View) Data Validation: LazAI leverages on-chain community-driven perspectives to ensure data reliability, alignment, and contextual accuracy.

By enabling on-chain proof of AI data integrity, LazAI ensures that developers, researchers, and enterprises can build AI models with confidence, free from manipulated, biased, or unverifiable datasets.

2.2 Decentralized AI Execution and Incentive Framework

Traditional AI models require extensive computational resources, making high-performance AI development expensive, inefficient, and limited to a few dominant players. The current ecosystem suffers from:

• Expensive Compute Costs – The dominance of centralized AI cloud providers results in high training and inference costs, limiting AI accessibility.

• Underutilized AI Resources – Existing AI platforms fail to optimize data sharing, model reusability, and computational efficiency.

LazAI’s Solution: AI Execution Layer & Verifiable Computing

LazAI introduces a scalable, decentralized execution model that ensures low-cost, high-performance AI training and inference:

• On-Chain Verified AI Execution: LazAI utilizes TEEs and ZKPs to ensure trustless AI data evaluation and model execution.

• Tokenized AI Incentive Mechanisms: Contributors (data providers, validators, and AI developers) receive DAT rewards, ensuring a sustainable and incentivized AI ecosystem.

With decentralized AI computing, LazAI democratizes access to AI training and inference, making AI more efficient, collaborative, and economically viable.

2.3 Composability-Driven AI Economy

The current AI economy remains siloed—models are locked behind APIs, datasets are gated by licensing, and interactions between different AI systems are difficult to orchestrate. This fragmentation severely limits innovation, especially in scenarios that require collaboration across multiple agents, domains, or stakeholders.

LazAI’s Solution: A Unified and Tokenized AI Marketplace

LazAI envisions a permissionless, composable AI economy where every AI asset—whether it’s a dataset, model, or an agent’s inference output—can be tokenized, verifiably exchanged, and reused across different contexts.

• DAT-Powered AI Assetization: With the DAT standard, LazAI enables every AI component to be treated as an on-chain asset. Each token anchors usage rights, provenance, share entitlements, and optional expiration, providing a standardized wrapper for trustless AI interaction.

• Composable AI Infrastructure: By unifying tokenized models, datasets, and agents under the same programmable interface, LazAI supports complex, multi-agent workflows. Agents can autonomously call each other’s services, build on top of one another’s outputs, or co-train using shared datasets, without needing centralized orchestration.

• Decentralized AI Marketplace: LazAI hosts an open marketplace for AI assets and services, allowing:

  • Individuals to monetize their datasets or fine-tuned models

  • Developers to compose multi-agent pipelines on-chain

  • Communities to curate domain-specific intelligence through iDAO governance

  • A new class of applications such as personal AI avatars, capable of evolving through market interactions, offering emotional support, knowledge exchange, or value generation

In this model, ownership, access, and collaboration become programmable, turning today’s static AI deployments into a dynamic, modular, and liquid ecosystem.

3. LazAI Verified Computing Framework

Ensuring the authenticity, integrity, and verifiability of AI data is critical to building a trustworthy AI ecosystem. LazAI has developed a decentralized, efficient, and scalable verification framework that integrates iDAO governance, Quorum-based validation. This framework provides a robust lifecycle verification process for AI datasets, training models, and inference results, ensuring that all AI-generated assets are trustless, transparent, and tamper-proof.

3.1 AI Data Verification Process

As shown in the picture above, the LazAI verification process follows a structured four-step validation flow that ensures data is securely recorded, verified, and continuously monitored within the LazAI Network. This process involves submission, registration, proof validation, and final verification with reward allocation.

Step 1: iDAO Submits LazAI Flow to Quorum

Each iDAO plays a pivotal role in the validation and governance of AI datasets and models. iDAOs are responsible for submitting LazAI Flow to their designated Quorum, a decentralized validation group that ensures AI data meets integrity and provenance standards.

LazAI Flow consists of:

• Dataset Metadata: Source, type, quality indicators, and integrity markers.

• Training Model Information: Parameters, architecture, and provenance to ensure AI reproducibility.

• Agent Metadata: AI agent execution parameters, behavioral logs, and validation history.

Once verified within the Quorum, this information is anchored on-chain, preventing manipulation and ensuring dataset ownership.

Step 2: LazChain Network Registers LazAI Flow & Generates LazAI Assets

Upon successful validation by the Quorum, the LazChain Network records the LazAI Flow as an immutable transaction, creating a permanent on-chain record of the AI dataset’s lineage. At this stage, the system generates corresponding LazAI Assets, which serve as tokenized representations of AI data (DATs).

These assets serve as proof-of-origin, data integrity markers, and programmable AI governance tools within the LazAI ecosystem.

Step 3: iDAO & Challengers Submit Verification Proofs

To maintain continuous integrity and prevent fraudulent AI data submissions, iDAOs and challengers engage in a verification proof process.

• iDAO submits Verification Proofs: Proving data authenticity, AI model accuracy, and inference correctness using cryptographic validation techniques.

• Challengers (Fraud Proof Validators) submit Fraud Proofs: If inconsistencies or malicious claims are detected, challengers can dispute dataset authenticity, training results, or inference outputs.

Fraud Proofs ensure that biased AI models, synthetic data manipulation, or compromised datasets are identified and penalized before they are used in critical applications.

Step 4: LazChain Network Verifies Proofs & Allocates Rewards

Once Proofs and Fraud Proofs are submitted, the LazChain Network runs a Verify Contract that executes multi-layered verification methods, including:

1. Off-Chain Proofs: iDAOs perform self-validation for efficient, low-cost verification.

2. Optimistic Proofs: Assumes submitted data is valid unless challenged by a fraud proof.

3. TEE or ZK Proofs: Provides cryptographic verification without exposing sensitive AI model data.

Reward & Penalty Allocation:

• Valid Verification Proofs: The submitting iDAO receives DAT rewards as an incentive for contributing trustworthy AI data.

• Successful Fraud Proofs: Challengers earn dispute resolution rewards, and the original submitter faces slashing penalties for fraudulent claims.

• Final Verification Record: All results are stored within the DAT ecosystem, ensuring transparency and traceability across the AI network.

3.2 Key Advantages of LazAI’s Verification Framework

The LazAI Verification Framework provides a trustless, decentralized approach to AI dataset validation, model verification, and fraud prevention, ensuring a scalable and secure AI-driven blockchain network.

1. Decentralized & Scalable: iDAO + Quorum-based validation prevents single points of failure and ensures data integrity without requiring central control.

2. Trustless AI Data Verifications: Ensure AI data remains verifiable, tamper-proof, and auditable without exposing raw data.

3. Efficient Dispute Resolution: Optimistic Proofs (OPs) reduce verification overhead, while Fraud Proof mechanisms ensure a secure challenge-response validation system.

4. Incentive-Driven Ecosystem: DAT rewards incentivize high-quality AI data submissions, while slashing mechanisms discourage false claims, ensuring an economically sustainable verification system.

3.3 Conclusion

By combining economic incentives, cryptographic proofs, and decentralized AI governance, LazAI provides a robust verification standard that supports secure, scalable, and verifiable AI ecosystems for Web3 and beyond.

4. LazAI DAT

The Data Anchoring Token (DAT) is a semi-fungible token (SFT) standard developed by LazAI to represent AI-native digital assets. Unlike general-purpose token formats, DAT integrates three essential properties into a unified structure:

• Ownership Certificate – Proof of contribution or claim over datasets, models, or computation results;

• Usage Right – Access quota to invoke AI services, such as agent execution or model calls;

• Value Share – Economic entitlement to future revenue, proportional to the token’s value and shareRatio.

With a Class-based architecture, value-based metering, and on-chain verifiability, DAT enables:

• Composable AI datasets and modular agents

• Tokenized inference and usage-based access

• Royalty-backed economic models for AI contributors

This standard serves as the core abstraction for AI assets in LazAI, supporting programmable licensing, fine-grained rights enforcement, and seamless integration with the broader AI data economy. It represents a next-generation framework that moves beyond static NFTs or ERC-20s — optimized for the dynamic, evolving world of decentralized AI.

4.1 Key Design Features of DAT

The Data Anchoring Token (DAT) is a novel semi-fungible asset format tailored for decentralized AI applications. Each DAT token represents a dynamic bundle of:

• Ownership Certificate – Provenance and authorship of AI datasets, models, or inferences

• Usage Right – Access quota for invoking AI services

• Revenue Share – A programmable entitlement to future rewards

To enable scalability and composability, DAT follows a class-based architecture:

Class-Based Structure

Each class represents an AI asset category (e.g., Dataset, Model, Agent) with metadata including:

• Descriptive name and URI

• Hash-based proof for integrity

• Optional expiration or policy constraints

Minting and Value Parameters

When issuing a DAT, the following key fields are defined:

Programmable Operations

• Internal Value Transfer: DATs of the same class can exchange partial value via transferValue, enabling fine-grained utility sharing across agents or users.

• Class-Level Approvals: With approveForClass, holders can delegate operational control to contracts or platforms.

Integrated Revenue Sharing

Revenue from AI agent usage can be automatically split among token holders based on shareRatio, optionally routed through a settlement contract. This eliminates the need for off-chain reconciliation and ensures on-chain traceability.

4.2 DAT Lifecycle Example

Step 1: Define an AI Asset Class

Register a new category for AI assets (e.g., datasets, models).

• Assign a unique class ID (e.g., 1) to identify the asset category.

• Name the class (e.g., "Medical Dataset") and describe its purpose (e.g., "Open-source dataset for disease classification").

• Store metadata (e.g., asset details, usage rights) on a decentralized storage system like IPFS, and link it to the class using a URI (e.g., ipfs://metadata/med-dataset-class)

Step 2: Mint DAT Tokens (Bind and Issue Assets)

Create tokens representing ownership or access to a specific AI asset within a class.

• Specify the recipient (user or address) who will hold the token (e.g., user1).

• Link the token to the asset class using its class ID (e.g., 1 for "Medical Dataset").

• Define the token’s value (e.g., 1000 units with 6 decimal places for precision).

• Set a revenue share ratio (e.g., 5%, represented as 500 in a 10,000-scale system) for distributing future earnings.

• Optionally, set an expiration time (e.g., 0 for no expiration).

Step 3: Service Payment (Agent Invocation)

Pay for AI services using DAT tokens.

• Transfer tokens from a user’s wallet (e.g., user1’s token ID) to a designated treasury (e.g., an agent’s contract address).

• Specify the payment amount (e.g., 100 units) for using the agent’s services.

• Support flexible billing models:

  • Pay-as-you-use: Directly charge for each service invocation.

  • Delegated billing: Allow third parties (e.g., employers) to pay on behalf of users.

Step 4: Revenue Share Demonstration (Future Extension)

Distribute earnings generated by the AI agent to token holders.

• When the agent earns revenue (e.g., 10 USDC from service fees), the contract calculates each token’s share based on its revenue share ratio.

• For example, a token with a 5% ratio receives 0.5 USDC (5% of 10 USDC).

• Automatically transfer the proportional revenue to each token holder’s wallet.

Step 5: Token Expiration (Optional)

Manage time-bound access to AI assets (e.g., subscriptions)

• Set an expiration timestamp for a token (e.g., after 1 year).

• When the timestamp is reached, the token’s access rights to the AI asset are revoked automatically.

• Use cases:

  • Subscription-based models (e.g., access to a premium medical dataset for 3 months).

  • Time-limited licenses for AI tools.

5. LazAI Quorum-Based BFT

LazAI’s Quorum-Based BFT (QBFT) consensus is a modular and scalable consensus protocol optimized for AI-centric decentralized systems. It blends practical Byzantine Fault Tolerance (PBFT) with a Quorum-based voting mechanism to ensure efficient validation, integrity, and liveness in a multi-agent AI data network.

5.1 Key Actors inside LazAI’s QBFT Layer

Rather than a generic “validator set,” LazAI organises consensus around Quorums — small, domain-focused collectives that both validate blocks and curate AI data.

Why the split?

• Quorums supply economic security and domain expertise (e.g., medical-data quorum vs. DeFi-model quorum).

• Challengers keep everyone honest without bloating the fast path of consensus.

5.2 VSC (Verifiable Service Coordinator)-Based iDAO-Quorum Interaction Protocol

Security Delegation via Stake-Based Quorum Integration

Each Quorum node participating in LazChain’s consensus mechanism is required to stake native tokens as a guarantee of honest behavior. Through this staking model and potential external collaborations (e.g., restaking, cross-chain validation, or inter-protocol delegation), iDAOs indirectly inherit the economic security of LazChain.

POV/Model/Agent Updates Are Transmitted via VSC to Quorum for Consensus

Whenever an iDAO performs updates—whether submitting new POV Inlet data, publishing a model, or deploying an AI Agent—these changes are packaged as service transactions and routed to the relevant Quorum via the VSC protocol. Each Quorum, operating under a Byzantine Fault Tolerant (BFT) consensus, independently validates and reaches agreement on the transaction outcome before anchoring them to LazChain.

Proof Submission & Asynchronous Validation via VSC

After consensus on the high-level update, VSC asynchronously dispatches verification artifacts—such as ZK proofs, Optimistic Proofs, or TEE attestations—to the relevant Quorum nodes. These proofs serve as cryptographic evidence that the update was generated under valid computational assumptions and that the iDAO’s declared actions were faithfully executed.

Challenger Arbitration and Slashing Procedure

Within each Quorum, a rotating set of Challenger nodes is elected to perform near real-time audits. These nodes continuously pull iDAO-submitted data and associated proofs from LazChain. If a Challenger detects inconsistency—such as an inference proof not matching the declared model weights, it can trigger a slashing dispute.

This initiates the following:

• Immediate freeze of the suspicious iDAO update.

• Verification of the challenger’s claim through multi-round consensus.

• If valid, slashing of:

  • Staked tokens by the responsible Quorum node (if it facilitated an invalid consensus).

  • DAT assets or usage credits associated with the offending iDAO.

5.3 Quorum-Based BFT Protocol (QBFT)

Quorum-as-Validator: BFT Participation

Each Quorum in LazAI is treated as a full validator node in the BFT consensus layer of LazChain. Quorums participate in ordering and validating transactions related to AI datasets, models, and inference proofs.

• BFT Layer: Built on a Byzantine Fault Tolerant consensus mechanism, where Quorums serve as the proposers, voters, and committers.

• Quorum ID: Each Quorum has a registered QuorumID and validator weight based on its staking level and historical performance.

• Deterministic Rotation: Block proposal is rotated across Quorums; performance and slashing affect rotation weights.

iDAO ↔ Quorum: Trust-Coupling via Economic Bonding

Each iDAO must establish explicit trust relationships with one or more Quorums to publish and validate AI assets. Two flexible trust modes are supported:

• Restaking Mode: iDAO stakes native tokens (e.g., $LAZ) to the target Quorum, delegating verification responsibility. Slashing penalties apply for fraud or invalid proofs.

• DAT-Backed Trust Mode: iDAO may mint AI assets (e.g., datasets or models) as DATs and request endorsement by a Quorum. In this mode:

  • The Quorum acts as a verifier and partial staker of the DAT.

  • The DAT becomes slashing-enabled: provable fraud leads to partial revocation or burn of DAT value.

  • Revenue sharing can be jointly configured between iDAO and Quorum based on the shareRatio.

Quorum as a Hash-Proven Off-Chain Storage Gateway

Quorums are not only validators, but also serve as off-chain AI storage coordinators. They host:

• Raw datasets (IPFS/Arweave/Filecoin),

• Fine-tuned models,

• Inference results, execution logs, and

• OP/ZK/TEE-based proofs.

Only the corresponding hash commitments and metadata are posted on-chain to minimize LazChain storage load.

iDAOs fetch training data from Quorums and submit updates via Verifiable Service Coordinator (VSC).

VSC: Orchestrated Trustless Coordination

The Verifiable Service Coordinator (VSC) bridges iDAO outputs and Quorum consensus:

• Transaction Submission: iDAO sends POV Updates, Model Anchors, Inference Outputs, and Verification Proofs to the VSC.

• Proof Dispatching: VSC asynchronously dispatches proof bundles (e.g., OP/ZK/TEE) to corresponding Quorums.

• Quorum Consensus: Quorums validate the bundles and finalize them on LazChain via BFT.

Challenger-Based Slashing Protocol

To ensure iDAO integrity and data authenticity, Challenger nodes are elected from within each Quorum:

• Near-Real-Time Monitoring: Challengers continuously pull Quorum-endorsed proofs from LazChain.

• Fraud Detection: If an iDAO is found to have submitted a model/proof inconsistent with the training dataset or usage policy:

  • A fraud proof can be submitted.

  • If verified, the iDAO is slashed (token stake or DAT-backed value).

  • The challenger is rewarded.

• Slashing Scope:

  • Native token slashing from restaking.

  • DAT shareRatio burn from endorsement.

  • Temporary blacklist from specific Quorums.

Innovation Points vs Traditional BFT

6. Conclusion

LazAI redefines the artificial intelligence landscape by integrating blockchain technology, verifiable computing, and decentralized governance, addressing three fundamental challenges plaguing traditional AI: barriers to data sharing, lack of standardized quality evaluation, and inequitable value distribution.

By centering data ownership, verifiability, and fair rewards, it bridges the gap between cutting-edge technology and ethical AI development. As more contributors, developers, and industries join its ecosystem, LazAI is poised to transform AI from a tool of the few into a shared intelligence infrastructure for all, defining the next era of decentralized AI.