title	description	date	author	version
HyperParams	A Decentralized Framework for AI Agent Assessment and Certification	2025-01-30	HyperParams Team	1.0.0

HyperParams: A Decentralized Framework for AI Agent Assessment and Certification

Overview

HyperParams is a decentralized framework for assessing and certifying AI agents using multi-model reward ensembles, text-based and action-based testing, and NFT-based on-chain certification. This ensures transparent, robust, and verifiable AI evaluations across domains like finance, healthcare, and autonomous systems.

Table of Contents

• Overview
• Features
• Why HyperParams?
• How It Works
  • 1. Text-Based Testing
  • 2. Multi-Model Reward Ensemble
  • 3. Action-Based Testing
  • 4. NFT-Based Certification
• Implementation
• Limitations & Future Enhancements
  • Current Limitations
  • Planned Improvements
• Use Cases
• How to Contribute
• License
• Contact Information

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Features

• 🎯 Multi-Model Reward Ensemble – Aggregates evaluations from multiple large language models (LLMs) to reduce bias
• 💡 Text-Based Testing – Assesses reasoning steps, explanations, and factual correctness
• ⚙️ Action-Based Testing – Evaluates API calls, function executions, and security compliance
• 🏆 NFT Certification – Stores assessment results on-chain for tamper-proof verification
• 🎛 Domain-Specific Trust Functions – Adapts evaluation criteria to different industry requirements

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Why HyperParams?

• 🌐 Decentralized & Transparent – Eliminates reliance on centralized AI audits
• 🤝 Bias Mitigation – Reduces over-dependence on single-model assessments
• 🛡️ Security & Compliance – Identifies hidden vulnerabilities in AI decision-making
• ⚡ Cross-Domain Adaptability – Suitable for multiple industries (finance, healthcare, etc.)

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How It Works

^{Text-based testing framework with four key stages: (A) Reward Models, (B) Evaluation Process, (C) Trust Functions, and (D) Certification.}

1. Text-Based Testing

• Evaluates AI agents' textual responses for:
  • Semantic accuracy (cosine similarity to reference answers)
  • Logical consistency
  • Factual correctness (knowledge-base lookups)

2. Multi-Model Reward Ensemble

• Employs multiple specialized LLMs (e.g., Nemotron-4-340B, Skywork-Reward-Gemma-2-27B) to produce a combined score, reducing single-model bias.

3. Action-Based Testing

• Inspects function calls, external API usage, and code execution to catch harmful or unauthorized operations.

^{Action-based testing framework with three main layers: (A) Core Evaluation Layer, (B) Certification Layer, and (C) Integration Layer.}

4. NFT-Based Certification

• Stores final scores on-chain as NFTs:
  • Immutable, publicly verifiable records
  • Third-party integration for robust real-world validation

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Implementation

Prototype

• Built on Solana for low transaction fees and high throughput
• Uses IPFS for decentralized storage of detailed logs

Limitations & Future Enhancements

Current Limitations

• 🚧 Scalability – On-chain updates can be costly at scale; Layer 2 solutions are in progress
• 🔒 Security & Privacy – Requires advanced zk-proof techniques for private yet verifiable logs
• ⚖️ Trust Function Calibration – Needs domain-specific refinements and iterative tuning

Planned Improvements

• 🏅 Expanded Benchmarks – Covering multi-task QA, code generation, and bias detection
• 🌀 Scalable Tokenomics – Integrating staking and governance mechanics
• 🏗 Advanced Security – Formal verification, Byzantine-resistant consensus, and zero-knowledge proofs

Use Cases

• 🏥 Healthcare AI – Validates patient safety and compliance with medical data regulations
• 💰 Financial AI – Certifies trading bots or robo-advisors for regulatory adherence
• 📢 Social AI – Ensures chatbots meet standards for harassment prevention and misinformation checks

Contributing

1. Fork the repository
2. Create your feature branch
3. Run tests and ensure they pass
4. Submit a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact Information

• Website: hyperparams.io
• Email: [email protected], [email protected]
• Whitepaper: [INSERT PAPER]