Network & Flow

Cortensor's network architecture is designed to facilitate seamless interaction between different node types, ensuring efficient task execution and robust AI inferencing capabilities. This section provides a detailed overview of the network flow, illustrating how various components interact to deliver high-quality AI services.

Overview

The Cortensor network consists of several key components, each playing a specific role in maintaining the system's integrity, efficiency, and scalability. The network flow encompasses the processes from session creation to task completion and validation, ensuring a seamless user experience.

Network Components

Router Nodes:

• Act as intermediaries between users and miner nodes.

• Manage task allocation, session creation, and secure communication.

• Provide compatibility with Web2 (REST API) and Web3 (SDK) environments.

Miner Nodes:

• Perform AI inferencing tasks using various AI models.

• Range from low-end devices to high-end GPUs, ensuring inclusivity.

• Participate in validation processes to ensure result accuracy.

Clients/Users:

• Initiate sessions and submit AI inference requests.

• Interact with the network through smart contracts or REST API.

• Retrieve results and configure validation levels.

Oracle/Master Guard Nodes:

• Maintain block time consistency and validate tasks.

• Operate in a permissioned setting, transitioning to permissionless in the future.

• Govern network operations and ensure reliability.

Network Flow

Session Creation:

• Users create a session by depositing tokens, calculated in terms of LLM tokens.

• The session represents a subscription where users allocate assets for using AI inference services.

Prompt Submission:

• Users submit prompts to the network via smart contracts or REST API through router nodes.

• The router node verifies payment and other session parameters before processing the request.

Task Allocation:

• Router nodes analyze incoming requests and allocate tasks to the most suitable miner nodes based on their capabilities.

• The allocation process considers the type of model to be used, the node's performance, and the required response time.

Task Execution:

• Miner nodes perform the assigned AI inference tasks.

• Tasks are executed according to the specified model and parameters.

• Miners submit the results securely, ensuring data integrity and privacy.

Result Validation:

• Validation nodes or other miner nodes validate the results to ensure accuracy.

• Validation can be configured by the user during session creation, allowing for varying levels of thoroughness and cost.

• Validation methods include semantic checks, embedding comparisons, and checksum verifications.

Result Retrieval:

• Once validated, results are delivered to the user through smart contracts, REST API, or WebSocket.

• The router node facilitates the secure transmission of results, maintaining user privacy and data security.

Incentive Distribution:

• Miners and validators receive rewards based on their contributions to the network.

• Rewards are distributed in tokens, incentivizing participation and ensuring network stability.

Key Processes

Dynamic Task Matching:

• The router node dynamically matches inference requests to the capabilities of miner nodes.

• This process optimizes resource utilization and ensures timely task completion.

Proof of Inference (PoI):

• A consensus mechanism that validates task completion and ensures the reliability of AI inference.

• Involves collaborative efforts among nodes to complete and validate tasks, building a reputation system based on performance.

• Validates the similarity of completion among other inference nodes using the same model, measured by embeddings and vector distances to ensure nodes have performed the task correctly.

Proof of Useful Work (PoUW):

• Ensures the correctness and usefulness of AI inference results.

• Validates whether the generated information is useful and extendable as knowledge.

• Involves additional checks such as semantic consistency, logical coherence, or practical applicability.

• Validators provide feedback or scores on the results, helping to determine their usefulness.

Security and Privacy:

• Data transmission within the network is encrypted to maintain privacy.

• Nodes stake tokens to participate, ensuring commitment and reducing the likelihood of malicious behavior.