2. Decentralized Computing Network Architecture

The mAI computing network is based on distributed computing and builds a sustainable, scalable computational system through on-chain scheduling, verification, and settlement.

2. Decentralized Computing Network Architecture

2.1 Overall Structure of the mAI Computing Network

The mAI computing network is based on distributed computing and builds a sustainable, scalable computational system through on-chain scheduling, verification, and settlement. The network comprises computing nodes, verification nodes, storage nodes, task scheduling systems, on-chain runtime environments, and a unique mining pool contract, forming a complete loop from computation provision, task execution, result verification to reward distribution. All modules operate independently but follow the same immutable on-chain rules, maintaining stability and consistency under high concurrency and large-scale node participation.

At the architectural level, the unique mining pool is central to the computing economy. At genesis, it locked all 100M pre-minted tokens and permanently renounced ownership rights, preventing token minting, parameter modifications, token recall, or interference by any individual or organization. All computing rewards and node incentives are automatically released from the pool according to fixed rules, establishing a truly decentralized incentive structure.

The task scheduling system evaluates node performance, latency, and task suitability, then allocates tasks automatically. Tasks---from model training and inference to parameter calculations and data processing---are submitted via a standardized interface, allowing diverse computing resources to collaborate under unified standards and avoiding common resource waste or compatibility issues.

During execution, tasks are assigned to nodes, which upload results on-chain for verification. Once verified, the mining pool contract releases rewards according to work performed. The process is fully automated, with no manual intervention or parameter modification, ensuring transparency and auditability of computing records and incentives.

The network architecture excels in scalability and continuity. As node numbers grow, scheduling capacity scales linearly without performance degradation. Its distributed structure inherently avoids single points of failure, maintaining computational capacity even during node downtime, network fluctuations, or localized failures. Cross-chain collaboration interfaces allow future mAI computing to cover additional blockchain ecosystems, serving as multi-chain infrastructure.

2.2 Verifiable Computing Mechanism

The verifiable computing mechanism is the trust core, ensuring all node computations are authentic and tamper-proof. Traditional platforms rely on centralized servers, creating trust risks. mAI adopts a fully on-chain verification system where all results must pass on-chain validation before receiving rewards, establishing transparency.

Verification splits tasks and cross-checks results across multiple nodes. For complex or high-precision tasks, redundant multi-node execution enhances reliability. Nodes upload on-chain computation summaries containing task data, node attributes, execution time, and results. Unverified results receive no rewards, and node reputation is affected, potentially lowering task priority.

Reputation-weighted verification gives high-reputation nodes greater influence, reducing redundant verification and improving efficiency. New or low-reputation nodes undergo stricter verification to participate in rewards, balancing efficiency and security.

This mechanism enables a fully open computing market without centralized endorsement, with each computation having clear origin, verifiable process, and immutable record, laying the foundation for reward distribution, computing settlement, and node ranking.

2.3 Mining Pool Contract and Computing Record Model

The unique mining pool contract is the core of the computing economy, responsible for power statistics, contribution records, periodic settlement, and incentive release. All 100M pre-minted mAI tokens were deposited at genesis, with the pool permanently renouncing all authority---no minting, modification, or recall---ensuring automated, protocol-driven reward distribution.

The computing record model quantifies and records each node's valid computing power, task execution, verification results, and runtime on-chain. Rewards are aggregated per period, producing transparent, auditable contribution records.

Reward release rules:

POW reward: nodes completing AI tasks receive on-chain rewards automatically from the mining pool.

Node staking: nodes executing high-value tasks stake mAI to obtain eligibility for verification and additional rewards.

The mining pool cannot be modified or controlled, ensuring fully transparent reward distribution. Dynamic difficulty adjustment aligns incentives with computing supply-demand, maintaining fair competition across the network.

2.4 Node Types

The mAI computing network consists of three node types: computing nodes, verification nodes, and storage nodes. Together, they form the distributed computing ecosystem.

Computing nodes execute model training, inference, and data tasks, earning POW rewards and priority in high-value task allocation.

Verification nodes audit results on-chain and perform cross-verification, staking mAI to obtain verification rights and rewards.

Storage nodes handle on-chain/off-chain data storage, ensuring data availability, redundancy, and fast access.

All rewards originate from the unique mining pool, which cannot be modified, maintaining a real, stable, and sustainable economy.

2.5 On-Chain Runtime Environment and Execution Security Design

The on-chain runtime environment underpins task distribution, execution, verification, and reward settlement. To ensure correctness, the system implements standardized task specifications, verifiable task packaging, redundant execution, and reputation constraints, ensuring verifiability, traceability, and security.

All tasks execute via standardized interfaces to prevent hardware-induced deviations. Parameterized task packages prevent nodes from altering execution logic. Redundant execution and cross-verification deter malicious nodes. Nodes failing verification lose rewards and reputation, preventing future task participation.

The reputation system quantifies success rate, error rate, and responsiveness, prioritizing high-value tasks for high-reputation nodes, ensuring long-term network stability.

This systematic security design enables the mAI computing network to maintain high reliability and execution consistency without centralized control, supporting fair competition and sustainable growth across global computing resources.