OML: Open, Monetizable, and Loyal AI Model Distribution Framework

1. Introduction

Artificial Intelligence is transforming numerous domains from robotics and game-playing to mathematical reasoning and drug discovery. The emergence of powerful generative models like GPT series, OpenAI o3, and DeepSeek R1 represents a watershed moment in AI capabilities. However, the current paradigm of AI model distribution presents a fundamental dichotomy: models are either closed and API-gated, sacrificing transparency and local execution, or openly distributed, sacrificing monetization and control.

2. The Fundamental Distribution Problem

The AI distribution landscape is currently dominated by two conflicting approaches, each with significant limitations that hinder sustainable AI development.

2.1 Closed API Services

Platforms like OpenAI's GPT and Anthropic's Claude maintain complete control over model execution through public APIs. While enabling monetization and usage governance, this approach leads to:

• Monopolization and rent-seeking behaviors
• Significant privacy concerns
• Lack of user control and transparency
• Inability to verify model behavior or ensure data privacy

2.2 Open-weight Distribution

Platforms like Hugging Face enable unrestricted model distribution, providing transparency and local execution but sacrificing:

• Monetization capabilities for creators
• Usage control and governance
• Protection against model extraction
• Sustainable development incentives

3. OML Framework Design

OML introduces a primitive that enables models to be freely distributed for local execution while maintaining cryptographically enforced usage authorization.

3.1 Security Definitions

The framework introduces two key security properties:

• Model Extraction Resistance: Prevents unauthorized parties from extracting and replicating the core model functionality
• Permission Forgery Resistance: Ensures usage permissions cannot be forged or tampered with

3.2 Technical Architecture

OML combines AI-native model fingerprinting with crypto-economic enforcement mechanisms, creating a hybrid approach that leverages both cryptographic primitives and economic incentives.

4. Technical Implementation

4.1 Mathematical Foundations

The security guarantees are built on rigorous mathematical foundations. The model extraction resistance can be formalized as:

$\Pr[\mathcal{A}(M') \rightarrow M] \leq \epsilon(\lambda)$

where $\mathcal{A}$ is the adversary, $M'$ is the protected model, $M$ is the original model, and $\epsilon(\lambda)$ is a negligible function in security parameter $\lambda$.

The permission system uses cryptographic signatures:

$\sigma = \text{Sign}_{sk}(m || t || \text{nonce})$

where $sk$ is the private key, $m$ is the model identifier, $t$ is the timestamp, and nonce prevents replay attacks.

4.2 OML 1.0 Implementation

The implementation combines model watermarking with blockchain-based enforcement:

class OMLModel:
    def __init__(self, base_model, fingerprint_key):
        self.base_model = base_model
        self.fingerprint_key = fingerprint_key
        self.permission_registry = PermissionRegistry()
    
    def inference(self, input_data, permission_token):
        if not self.verify_permission(permission_token):
            raise PermissionError("Invalid or expired permission")
        
        # Embed fingerprint in output
        output = self.base_model(input_data)
        fingerprinted_output = self.embed_fingerprint(output)
        return fingerprinted_output
    
    def embed_fingerprint(self, output):
        # Implementation of AI-native fingerprinting
        fingerprint = generate_fingerprint(output, self.fingerprint_key)
        return output + fingerprint

5. Experimental Results

Extensive evaluation demonstrates OML's practical feasibility:

• Security Performance: Model extraction attacks reduced by 98.7% compared to unprotected models
• Runtime Overhead: Less than 5% inference time increase due to cryptographic operations
• Accuracy Preservation: Model accuracy maintained within 0.3% of original performance
• Scalability: Supports models up to 70B parameters with minimal performance degradation

6. Future Applications & Directions

OML opens new research directions with critical implications:

• Enterprise AI Deployment: Secure distribution of proprietary models to clients
• Research Collaboration: Controlled sharing of research models with academic partners
• Regulatory Compliance: Enforcing usage restrictions for sensitive AI applications
• Federated Learning: Secure aggregation of model updates in distributed training

7. References

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4. Radford, A., Wu, J., Child, R., et al. (2019). Language Models are Unsupervised Multitask Learners. OpenAI Technical Report.
5. TensorFlow Serving Architecture. (2023). TensorFlow Documentation.
6. Nakamoto, S. (2008). Bitcoin: A Peer-to-Peer Electronic Cash System.