3. Mining Consensus Mechanism

The hybrid PoW/AI mining model forms the core consensus architecture of the mAI network.

3. Mining Consensus Mechanism

3.1 Hybrid PoW/AI Mining Model

The hybrid PoW/AI mining model is the underlying consensus architecture of the mAI computing network. Its goal is not to rely solely on traditional hash computations to maintain on-chain operations, but to combine the basic PoW structure with real AI computational tasks, creating a mining system that provides both security and productive value. This model allows nodes, in addition to completing necessary on-chain computational work, to execute AI tasks, thereby contributing both security and usable computing power to the network. Mining thus becomes a productive computational activity rather than merely an energy-consuming process.

The model is designed with PoW as the source of network randomness and ordering assurance, while AI computation constitutes the primary component of verifiable work. Both workloads jointly define the node's effective contribution, with their proportions automatically adjusted by on-chain algorithms, without any centralized authority able to change them. As the network scales, the system progressively increases the weight of AI task contributions, shifting the overall computing power structure toward real computation and making mAI a true token mapping computing value.

The hybrid model employs a multi-dimensional evaluation system for nodes, including latency, task completion quality, hardware stability, historical execution success, and reputation scores. Mining rewards are thus determined not purely by hardware, but by a combination of computing capability and stability. The system compares submitted workloads with historical performance and automatically increases verification difficulty if abnormal patterns are detected, preventing any node from inflating computing power or forging work to gain undue rewards.

The model's key advantage is sustainability. Traditional PoW consumes massive resources for meaningless computations, whereas the hybrid PoW/AI model couples tasks with real AI needs, making computing power part of actual productivity. All recognized workloads correspond to on-chain verified computations and are rewarded via the unique mining pool. As more nodes join, network computing power naturally expands, and mining remains competitive and economically valuable long-term, forming the foundational structure of an AI computing production network.

3.2 Proof-of-Work AI-PoW

AI-PoW is the core workload design in the mAI computing network. Its purpose is to transform AI computation tasks into a verifiable proof-of-work structure, standardizing, quantifying, and recording computing contributions. Unlike traditional PoW relying on repetitive hashing, AI-PoW requires nodes to execute real AI computations, including matrix operations, model inference, image processing, and data analysis, as assigned by the task scheduling module. Each successfully executed task generates an on-chain proof, serving as the basis for reward distribution.

A critical feature of AI-PoW is authenticity verification. Each task generates a unique task digest. Upon completion, nodes submit a combined proof of the digest and execution results. Verification nodes perform re-computation, sampling, or data logic comparison according to task type to ensure results are genuinely produced by the node. Only upon verification does the task workload enter the reward model. Inconsistent results reduce the node's reputation score and block reward eligibility.

To enhance anti-fraud capabilities, AI-PoW introduces random perturbations for each computation, preventing caching or reuse of historical results. Tasks are fragmented and distributed across multiple nodes, with cross-validation to confirm results. This reduces single-node cheating opportunities and improves parallel task execution, increasing network throughput.

AI-PoW ensures mining reflects real productivity, giving mAI tokens a solid computational backing. All AI-PoW workloads are automatically rewarded by the unique mining pool according to on-chain rules, with no authority to modify, pause, or intervene in outputs, ensuring fully decentralized execution.

3.3 Mining Task Scheduling and Competition

The task scheduling mechanism is the core of the computing network, ensuring AI tasks are efficiently and appropriately allocated to suitable nodes. The scheduler dynamically selects nodes based on hardware performance, latency, task suitability, reputation, and historical records, maximizing resource utilization.

When task demand exceeds available computing power, a task competition mechanism is activated. Nodes submit competition parameters based on three criteria: computing performance (e.g., throughput, memory capacity, parallelism), task suitability (hardware capability for specific task types), and reputation weighting (stable contributors prioritized). This prevents low-quality nodes from monopolizing tasks and maintains overall execution quality.

The scheduler also integrates random distribution factors to prevent long-term task monopolization by specific nodes, preserving decentralization. Critical tasks are executed by multiple nodes with comparative validation, while routine tasks use a rapid allocation mode to enhance throughput.

The system supports cross-region node scheduling to reduce latency disparities, balancing task execution. Future iterations will leverage models to optimize task-node matching, extending computational capacity across broader scenarios. All scheduled tasks feed back into the unique mining pool as work input, with rewards automatically issued.

3.4 Computing Quality Scoring Mechanism

The computing quality scoring mechanism measures long-term node contributions and is central to task allocation and reward issuance. Node reputation scores are based on execution success, task latency, verification pass rate, stability, actual hardware performance, and violation records, aligning rewards with actual contributions.

The system prioritizes long-term contributions over hardware scale. A large GPU cluster with high failure rates will score lower and receive fewer high-value tasks, while a mid-sized, consistently performing node gains higher scores and task priority. Scores are recalculated each period, adjusting node capability and reward limits; severe violations may remove a node from AI-PoW eligibility, ensuring long-term network stability.

This scoring mechanism ensures fair, transparent rewards, directly tied to the unique mining pool, with incentives flowing only to genuinely contributing nodes, supporting self-regulation and network evolution.

3.5 Dynamic Mining Difficulty Adjustment

Dynamic difficulty adjustment balances network output and computing power demand by modulating task difficulty, reward multipliers, and thresholds. The system calculates difficulty using global computing power, verification node load, task completion times, and recent period outputs, maintaining predictable and stable token release.

Unlike traditional PoW, difficulty adjustment applies not only to on-chain workloads but also to AI-PoW task scales, sampling ratios, and reward intensity. Rapid network growth increases AI task difficulty or reduces reward multipliers; decreased power or surging demand lowers difficulty and boosts reward efficiency, encouraging more participation.

The difficulty mechanism interacts with node reputation. High-reputation nodes maintain efficiency during difficulty spikes, while low-reputation nodes are naturally filtered, ensuring network backbone quality. All adjustments are executed automatically on-chain, with rewards released by the pool according to updated difficulty, ensuring transparency.

3.6 Anti-Cheating and Verifiable Power Strategies

The network's openness necessitates strong anti-cheating mechanisms. mAI implements a five-layer anti-cheat system, requiring strict verification of every workload to qualify for rewards:

Task Randomization: each task digest contains random parameters, preventing caching or reuse.

Cross-Validation: critical tasks are executed by multiple nodes, with verification nodes confirming authenticity; discrepancies trigger secondary verification by reputable nodes.

Hardware Calibration: node performance is periodically checked against reported power; discrepancies reduce reputation.

Behavior Monitoring: records task skips, timeouts, falsification, or repeated submissions; accumulated anomalies limit node participation.

Economic Penalty and Participation Threshold: verifying nodes stake mAI; detected malicious behavior reduces stake and future eligibility, making cheating costlier than potential gain.

All verifications and penalties are recorded on-chain; rewards are automatically released from the unique mining pool based on actual workloads, ensuring a highly resilient and self-regulating computing economy.