Sahara Protocols

Abstractions & Metadata

At the heart of Sahara Blockchain's architecture lies a sophisticated system of abstractions that define how AI assets exist and interact on-chain. These abstractions create a standardized framework for representing everything from simple datasets to complex AI pipelines, ensuring consistent handling of ownership, licensing, and revenue distribution.

Core Abstractions

The Sahara platform implements three fundamental types of abstractions that work together to create a comprehensive system for AI asset management:

Account Abstraction

Every AI asset on Sahara is associated with a dedicated on-chain Account. This Account serves as the asset's digital identity and administrative hub, maintaining crucial relationships between the asset's various components. The Account manages:

• Metadata updates and version history

• Integration with protocol modules like licensing and revenue sharing

• Relationship tracking with other AI assets

• Access control and permissions

Metadata Abstraction

The metadata abstraction layer ensures comprehensive documentation of every AI asset's properties and lineage. This includes:

• Title and description

• Creation date and version history

• Owner identification

• Technical specifications

• Purpose and intended use cases

• Data characteristics and structure

• Copyright and licensing status

• Collection methodologies

• Creator attributions

Global Asset Registry

The Global Asset Registry stands at the core of Sahara Blockchain's AI asset management system. It functions as the authoritative source of truth for all AI-related assets on the network, implementing a sophisticated framework for tracking ownership, metadata, and relationships between different AI components. This registry does more than simply catalog assets—it creates a dynamic, interconnected ecosystem where AI development can occur transparently and collaboratively.

Registration Process

When developers register an AI asset on Sahara Blockchain, they initiate a multi-step process that establishes the asset's identity and governance structure on-chain. The registration process creates three fundamental elements that work together to manage the asset throughout its lifecycle.

First, the registry mints an Ownership NFT that represents absolute control over the asset. This NFT serves as the root-level authority for the asset, functioning similarly to a property deed in traditional systems. Unlike conventional NFTs, these Ownership NFTs carry special privileges within the Sahara ecosystem, enabling the holder to make administrative decisions about the asset's usage, licensing, and future development.

Second, the system creates a comprehensive metadata record that captures every relevant detail about the asset. This metadata serves both technical and administrative purposes, storing everything from basic descriptive information to complex technical specifications. The metadata structure is designed to evolve with the asset, maintaining a clear historical record while accommodating future updates and modifications.

Third, the registry establishes a dedicated on-chain account for the asset. This account acts as the operational center for all interactions involving the asset, managing everything from licensing agreements to revenue distribution. The account structure ensures that all transactions and modifications related to the asset occur within a controlled, auditable environment.

Metadata Management

The metadata system within the Global Asset Registry implements a flexible yet structured approach to documenting AI assets. Each asset's metadata record contains multiple categories of information:

Core Identification: includes the fundamental details that establish the asset's identity within the ecosystem. This encompasses the asset's title, creation date, and unique identifier. These fields form the basic reference point for all future interactions with the asset.

Technical Documentation: captures the specifications and capabilities of the asset. For models, this might include architecture details, training parameters, and performance metrics. For datasets, it covers data structure, size, and quality metrics. This technical metadata ensures that potential users understand exactly what they're working with.

Ownership and Control: information documents who has authority over the asset and how that authority can be exercised. This section maintains a clear record of ownership transfers and includes any governance parameters that affect how the asset can be managed.

Relationship Mapping: tracks how the asset connects to others within the ecosystem. This becomes particularly important for derivative works, where new models or datasets build upon existing ones. The relationship mapping ensures proper attribution and revenue sharing across the development chain.

Dynamic AI Assets

The registry's account system provides ongoing management capabilities that extend well beyond initial registration. Through their dedicated accounts, assets can participate in various protocol activities:

• Licensing and Access Control: Asset owners can create and modify licensing terms, controlling how others can use their assets. The account system enforces these terms automatically, ensuring compliance without requiring constant oversight.

• Revenue Distribution: When assets generate value through usage or licensing, the account system manages the automatic distribution of revenues according to predetermined sharing agreements. This ensures that all contributors receive their fair share of generated value.

• Relationship Management: As assets evolve and interact with others in the ecosystem, their accounts maintain clear records of these relationships. This tracking becomes essential for managing derivative works and ensuring proper attribution throughout the development chain.

Licensing & Permissions

The Sahara Blockchain implements a sophisticated licensing system that enables AI asset owners to define, issue, and enforce usage rights through smart contracts. This system brings traditional licensing concepts into the web3 era, creating programmable legal frameworks that automatically enforce terms while enabling flexible monetization strategies.

License Structure

The licensing protocol builds upon three core components that work together to create a comprehensive rights management system. These components establish clear rules for how AI assets can be used while ensuring automated enforcement of terms and conditions.

First, License Templates provide standardized frameworks that asset owners can use as starting points. These templates cover common licensing scenarios such as commercial usage rights, derivative work permissions, and geographic restrictions. By offering pre-defined templates, we reduce the complexity of creating legally sound licensing agreements while ensuring consistency across the ecosystem.

Second, License Terms transform these templates into specific, immutable rules for individual assets. When an asset owner creates a license, they define exact parameters that govern how their asset can be used. These terms are permanently recorded on-chain, creating an unambiguous record of rights and restrictions.

Third, License Tokens represent active licenses as ERC-721 NFTs. These tokens serve as verifiable proof of rights, containing both the license terms and the technical mechanisms for enforcing them. Each token acts as a digital key, granting its holder specific permissions while maintaining a clear record of usage rights.

Types and Use Cases

The Sahara licensing system supports several distinct license types, each designed for specific use cases:

Partnership Licenses: facilitate long-term collaborative relationships. These licenses typically implement revenue-sharing models where both the asset owner and licensee benefit from successful utilization of the asset. Terms can be customized to reflect complex business arrangements while maintaining automated enforcement through smart contracts.

API Licenses: manage programmatic access to AI assets. They implement usage-based pricing models where licensees pay per call or computation. These licenses include built-in authentication mechanisms and can automatically track usage metrics for billing purposes.

Full Access: Licenses grant comprehensive rights to the asset through a one-time payment. These licenses might give the holder complete access to model parameters, training data, or other internal components. They're particularly useful for scenarios where deep integration or modification is needed.

Long-term Licenses: provide unlimited usage rights for a fixed time period. These licenses balance the flexibility of full access with temporal restrictions, making them ideal for project-based implementations or trial periods.

License Administration

Asset owners maintain significant control over their licensing strategy through several administrative functions:

Template Customization: allows owners to modify standard templates to create specialized licensing frameworks that meet their specific needs. While maintaining the basic structure that ensures compatibility with the broader ecosystem, owners can adjust parameters to implement unique business models.

Term Management: enables updating certain license parameters even after issuance, provided such modifications are permitted by the original terms. This flexibility allows licenses to evolve with changing requirements while maintaining the integrity of core agreements.

Usage Monitoring: provides detailed insights into how licensed assets are being utilized. Owners can track usage patterns, monitor compliance, and adjust their licensing strategy based on real-world implementation data.

Through this comprehensive licensing framework, Sahara Blockchain ensures that AI asset owners can effectively manage and monetize their intellectual property while maintaining control over how their assets are used and distributed within the ecosystem.

Ownership Attribution

The Sahara Blockchain implements a sophisticated attribution system that maintains a comprehensive record of relationships between AI assets throughout their development lifecycle. This system ensures transparent provenance tracking, proper credit allocation, and automated enforcement of licensing terms and revenue sharing across complex AI development chains.

AI Asset Relationships

At its core, the attribution system uses a series of standardized relationship flags to document how different AI assets connect to and derive from one another. These relationships create a verifiable chain of provenance that maps the evolution of AI assets across the platform.

The TRAINED_ON relationship serves as the fundamental link between models and their training data. When a developer creates a new AI model, they must specify which datasets were used in the training process. This relationship ensures that dataset creators receive proper attribution and compensation when their data contributes to successful models. For example, if a language model generates revenue through commercial licensing, the creators of its training datasets automatically receive their share based on pre-defined revenue sharing agreements.

The FINETUNED_FROM relationship tracks the lineage between specialized models and their parent models. This relationship is particularly important in the modern AI landscape, where many valuable models are created by fine-tuning existing base models for specific applications. When a developer fine-tunes a model, the system automatically inherits licensing terms and revenue obligations from the parent model, ensuring that original model creators maintain their rights while enabling innovation.

The DERIVED_FROM relationship captures broader forms of derivation that go beyond simple fine-tuning. This might include cases where a new model architecture incorporates significant elements from an existing model, or where multiple models are combined to create a new composite system. This relationship ensures that even complex derivation chains maintain clear attribution and proper revenue distribution.

Automated License and Revenue Management

The attribution system does more than simply record relationships—it actively enforces licensing terms and manages revenue distribution based on these relationships. When a new asset is created, the system automatically:

• Inherits and enforces relevant licensing restrictions from parent assets

• Establishes revenue sharing arrangements based on contribution levels

• Creates an immutable record of the asset's provenance

• Sets up automated distribution of future revenues

This automation ensures that attribution and compensation occur reliably without requiring manual intervention or oversight. For instance, if a fine-tuned model generates revenue through API calls, the system automatically distributes portions of that revenue to the original model creators and dataset providers based on their pre-defined sharing agreements.

Future-Proofing Ownership Attribution

The attribution system has been designed to accommodate the evolving nature of AI development. Its flexible architecture can handle new types of relationships as novel forms of AI collaboration emerge. The system supports:

• Multiple parallel relationship types between assets

• Complex chains of derivation and influence

• New relationship categories as needed

• Detailed metadata about each relationship

This forward-looking design ensures that the attribution system can continue to provide comprehensive tracking as AI development practices evolve and new forms of collaboration emerge.

Through this sophisticated attribution system, Sahara Blockchain ensures that everyone who contributes to AI development receives proper credit and compensation for their work, while maintaining clear records of how AI assets evolve and relate to each other across the ecosystem.

Revenue Sharing

Sahara Blockchain implements a sophisticated revenue sharing system that ensures fair compensation for all contributors in the AI development chain. This system combines traditional financial concepts with blockchain technology to create transparent, automated revenue distribution across complex networks of AI assets and their creators.

Asset Capitalization Structure

The platform uses three distinct instruments to represent different aspects of AI asset ownership and revenue rights:

Receipts: Establishing Core Ownership

Receipts serve as the foundational proof of AI asset ownership. These non-transferable, non-fungible tokens establish an immutable record of creation and ownership. Beyond simple proof of ownership, receipts contribute to a reputation system within the Sahara ecosystem.

Developers who create high-quality assets build stronger on-chain reputations, leading to increased visibility and opportunities within the ecosystem.

Shares: Managing Revenue Rights

Shares represent the right to participate in an asset's revenue stream. These on-chain tokens implement proportional revenue sharing, where holding a percentage of shares entitles the holder to that same percentage of generated revenue. Before asset creation, developers and knowledge providers establish clear parameters for share distribution, ensuring transparent and fair revenue allocation from the start.

Licenses: Controlling Usage Rights

Licenses define how others can use and build upon AI assets. Through the licensing system, asset owners can create multiple revenue streams while maintaining control over their intellectual property. License fees flow through the revenue sharing system, ensuring automatic distribution to all stakeholders.

Revenue Distribution Framework

The revenue distribution system implements sophisticated mechanisms for managing how value flows through the ecosystem:

Revenue Policies

The system supports two primary types of revenue policies:

Fixed Percentage Distribution: creates straightforward revenue sharing arrangements where parent assets receive predetermined portions of all generated revenue. This model provides predictable income streams for original creators and contributors.

Dynamic Percentage Distribution: allows for more complex revenue sharing models that can adjust based on various conditions such as usage volume, time periods, or performance metrics. This flexibility enables sophisticated business models while maintaining automatic enforcement.

Revenue Flow Management

Revenue distribution occurs automatically whenever an asset generates income through:

• License sales or renewals

• Usage fees from API calls or compute time

• Direct tips or contributions

• Secondary market transactions

The system carefully tracks these revenue streams and ensures proper distribution according to established policies. For assets with multiple contributors or parent relationships, the distribution follows a carefully calculated waterfall structure that respects all established sharing agreements while preventing over-allocation.

Execution Layer

The Sahara AI Execution Layer forms the operational backbone of the Sahara platform, providing the critical infrastructure needed to run AI workloads in a decentralized environment. This sophisticated system bridges the gap between on-chain asset management and real-world AI operations, ensuring secure, efficient, and verifiable execution of AI tasks.

Core Infrastructure Design

The Execution Layer is built on three fundamental principles that govern its operation and performance characteristics. First, the system maintains expedient processing by coordinating computations across a distributed network of contributors and participants. This coordination ensures that AI workloads are executed with minimal latency while maintaining high reliability.

Second, the infrastructure implements elastic scaling to handle varying computational demands. Through robust autoscaling mechanisms, the system can dynamically adjust its resource allocation in response to changing workload requirements. This elasticity ensures that the platform remains responsive and efficient even during periods of high demand.

Third, the system is designed for resilience, incorporating comprehensive fault tolerance mechanisms. Working in concert with the Sahara Blockchain, the Execution Layer maintains partition tolerance, allowing it to continue operating effectively even when parts of the network experience issues. In the event of failures, the system implements rapid recovery procedures to maintain workflow integrity and minimize service disruptions.

API and Model Management

The Execution Layer implements a sophisticated API system that serves as the primary interface between developers and the platform's computational resources. This API manages all aspects of model deployment and execution, providing developers with streamlined access to a growing library of public models including leading open-source options like Llama, Mistral, and Gemma.

Through this API, developers can:

• Browse available models with detailed specifications

• Access comprehensive version histories and performance metrics

• Deploy models with specific resource requirements

• Monitor deployment status and performance metrics

• Manage computational resource allocation

The API maintains high performance through careful optimization:

• Request handling is designed for minimal latency

• The system supports large numbers of concurrent operations

• Resource allocation occurs dynamically based on demand

• Performance metrics are continuously monitored and optimized

Smart Contract Integration

The Execution Layer maintains close integration with the Sahara Blockchain through a sophisticated network of smart contracts. These contracts manage the critical interface between on-chain governance and off-chain execution, ensuring that all computational activities maintain proper authorization and record-keeping.

The smart contract system handles several critical functions:

• Processing deployment requests from authorized parties

• Triggering resource allocation for approved operations

• Recording execution results and performance metrics

• Managing access controls and usage permissions

• Maintaining audit trails of all operations

Deployment and Execution Management

The Execution Layer implements a comprehensive system for managing AI model deployments and executions. This system operates through three primary components:

The Graph Node infrastructure provides efficient indexing and querying of on-chain data, enabling rapid access to deployment configurations and historical performance data. These nodes maintain synchronized copies of relevant blockchain data, ensuring that execution decisions are based on current information.

The Off-Chain Scheduler manages the complex task of resource allocation, determining how to distribute computational workloads across available resources. This component implements sophisticated scheduling algorithms that consider factors such as:

• Current resource availability

• Performance requirements

• Cost optimization

• Geographic distribution

• Redundancy needs

The core Execution Layer monitors blockchain events and manages the actual deployment and execution of AI workloads. Upon detecting deployment requests, it:

• Validates the request against on-chain permissions

• Allocates appropriate computational resources

• Initializes the required runtime environment

• Monitors execution progress and performance

• Records results and performance metrics on-chain

• Returns deployment confirmation and access details

Through this sophisticated infrastructure, the Execution Layer ensures that Sahara can provide reliable, scalable, and verifiable AI computation while maintaining the decentralized principles core to the platform's mission.