Abstract: Lumoz has completed an upgrade to its core AI architecture, launching a language-interactive Web3 interface and enabling semantic execution of on-chain tasks based on the MCP (Model Context Protocol) framework. Meanwhile, the team is developing a secure transaction initiation mechanism and exploring deeper integration between AI Agents and blockchain infrastructure.

Lumoz's AI upgrade is built around three key layers: the Understanding Layer, the Reasoning Layer, and the Execution Layer. The Understanding Layer uses natural language processing to convert user commands into structured on-chain intents. The Reasoning Layer applies task decomposition algorithms to break down complex operations into executable steps. The Execution Layer is deeply integrated with a modular Rollup environment to ensure AI decisions are accurately carried out on-chain.

The core advantage of this layered architecture lies in its modular decoupling. Each layer's technical components can be independently optimized and upgraded, avoiding the technical debt issues commonly seen in traditional monolithic systems.

Traditional AI Agents are primarily designed for Web2 scenarios and struggle to adapt to blockchain-specific challenges such as transaction latency, gas fee volatility, and network congestion. To address this, Lumoz has developed a native AI Agent architecture tailored for blockchain environments, integrating key modules for state awareness, risk assessment, and execution optimization:

• State Awareness Module: Monitors network conditions, contract status, gas fluctuations, and more in real time.

• Risk Assessment Module: Anticipates transaction risks to enhance interaction security.

• Execution Optimization Module: Dynamically adjusts transaction parameters to balance efficiency and cost.

The primary challenge of AI-initiated transactions lies in defining the boundaries of trust. In traditional models, users retain explicit control over every transaction; however, in AI-assisted scenarios, ensuring transparency and auditability of AI decisions becomes critical.

Lumoz addresses this by introducing an "Intent Verification" mechanism. Before initiating any transaction, the AI outputs a detailed execution plan and expected outcomes, allowing users to review and approve actions in advance. Additionally, the system records the AI's decision-making path, ensuring full traceability.

Solution 1: Offline Transaction Construction Model

This approach fully preserves the integrity of the existing security model. The AI system acts as a transaction builder, generating unsigned transaction data in a standardized format. Users then sign the transactions using their existing wallet tools, ensuring the security of their private keys.

Solution 2: Parameterized Page Redirection Model

This method is better suited for complex DeFi operations, such as multi-step liquidity provision or advanced derivatives trading. Once the AI identifies the user's intent, it automatically assembles the required parameters for the target dApp and redirects the user via a standardized interface.

Traditional AI model training requires massive centralized computing resources, which naturally conflicts with the decentralized philosophy of blockchain. Lumoz explores a decentralized training framework based on federated learning and zero-knowledge proof technologies. Participating nodes use zero-knowledge proofs to verify the correctness of their computations without exposing the underlying training data or model parameters. This design protects data privacy while ensuring fairness and transparency in model training.

At the core of this system is a standardized AI capability interface protocol. Developers create functional plugins according to a unified specification, which AI Agents can dynamically load and invoke. This architecture resembles the driver model of operating systems—ensuring system stability while enabling functional extensibility.

Lumoz’s AI strategy upgrade reflects the broader trend of Web3 infrastructure evolving from functionality to intelligence. By deeply integrating AI capabilities into all aspects of blockchain operations, Lumoz is shaping a more user-friendly and intelligent on-chain interaction paradigm.

The core value of this technical approach lies in lowering the entry barrier to Web3 while preserving its foundational principles of decentralization and security. As the technology continues to mature, we can expect to see more language-driven on-chain operations, along with smarter asset management and risk control mechanisms.

From a technological evolution standpoint, Lumoz’s AI upgrade is not merely an expansion of product features—it represents a fundamental reconfiguration of Web3 interaction models. This transformation is set to propel the entire industry toward a more intelligent and user-centric future.

Abstract: Lumoz has completed an upgrade to its core AI architecture, launching a language-interactive Web3 interface and enabling semantic execution of on-chain tasks based on the MCP (Model Context Protocol) framework. Meanwhile, the team is developing a secure transaction initiation mechanism and exploring deeper integration between AI Agents and blockchain infrastructure.

Lumoz's AI upgrade is built around three key layers: the Understanding Layer, the Reasoning Layer, and the Execution Layer. The Understanding Layer uses natural language processing to convert user commands into structured on-chain intents. The Reasoning Layer applies task decomposition algorithms to break down complex operations into executable steps. The Execution Layer is deeply integrated with a modular Rollup environment to ensure AI decisions are accurately carried out on-chain.

The core advantage of this layered architecture lies in its modular decoupling. Each layer's technical components can be independently optimized and upgraded, avoiding the technical debt issues commonly seen in traditional monolithic systems.

Traditional AI Agents are primarily designed for Web2 scenarios and struggle to adapt to blockchain-specific challenges such as transaction latency, gas fee volatility, and network congestion. To address this, Lumoz has developed a native AI Agent architecture tailored for blockchain environments, integrating key modules for state awareness, risk assessment, and execution optimization:

• State Awareness Module: Monitors network conditions, contract status, gas fluctuations, and more in real time.

• Risk Assessment Module: Anticipates transaction risks to enhance interaction security.

• Execution Optimization Module: Dynamically adjusts transaction parameters to balance efficiency and cost.

The primary challenge of AI-initiated transactions lies in defining the boundaries of trust. In traditional models, users retain explicit control over every transaction; however, in AI-assisted scenarios, ensuring transparency and auditability of AI decisions becomes critical.

Lumoz addresses this by introducing an "Intent Verification" mechanism. Before initiating any transaction, the AI outputs a detailed execution plan and expected outcomes, allowing users to review and approve actions in advance. Additionally, the system records the AI's decision-making path, ensuring full traceability.

Solution 1: Offline Transaction Construction Model

This approach fully preserves the integrity of the existing security model. The AI system acts as a transaction builder, generating unsigned transaction data in a standardized format. Users then sign the transactions using their existing wallet tools, ensuring the security of their private keys.

Solution 2: Parameterized Page Redirection Model

This method is better suited for complex DeFi operations, such as multi-step liquidity provision or advanced derivatives trading. Once the AI identifies the user's intent, it automatically assembles the required parameters for the target dApp and redirects the user via a standardized interface.

Traditional AI model training requires massive centralized computing resources, which naturally conflicts with the decentralized philosophy of blockchain. Lumoz explores a decentralized training framework based on federated learning and zero-knowledge proof technologies. Participating nodes use zero-knowledge proofs to verify the correctness of their computations without exposing the underlying training data or model parameters. This design protects data privacy while ensuring fairness and transparency in model training.

At the core of this system is a standardized AI capability interface protocol. Developers create functional plugins according to a unified specification, which AI Agents can dynamically load and invoke. This architecture resembles the driver model of operating systems—ensuring system stability while enabling functional extensibility.

Lumoz’s AI strategy upgrade reflects the broader trend of Web3 infrastructure evolving from functionality to intelligence. By deeply integrating AI capabilities into all aspects of blockchain operations, Lumoz is shaping a more user-friendly and intelligent on-chain interaction paradigm.

The core value of this technical approach lies in lowering the entry barrier to Web3 while preserving its foundational principles of decentralization and security. As the technology continues to mature, we can expect to see more language-driven on-chain operations, along with smarter asset management and risk control mechanisms.

From a technological evolution standpoint, Lumoz’s AI upgrade is not merely an expansion of product features—it represents a fundamental reconfiguration of Web3 interaction models. This transformation is set to propel the entire industry toward a more intelligent and user-centric future.