README.md

---
license: apache-2.0
license_name: apache-2.0
license_link: https://www.apache.org/licenses/LICENSE-2.0.txt
---

# Model Card: AGI Validator System

## Model Details

### Model Description
The AGI Validator is an advanced artificial general intelligence system for validating universal knowledge claims. It integrates multiple reasoning modes, evidence analysis, and real-time data verification to assess the validity of claims across various knowledge domains.

- **Developed by:** AI Research Team
- **Model type:** Hybrid Reasoning System
- **Language(s):** Python 3.10+
- **License:** Apache 2.0
- **System components:** 
  - Multi-Consensus Protocol (mCP) integration
  - Evidence quality assessment
  - Bayesian/causal/deductive reasoning engines
  - Real-time data integration
  - Domain-specific constraint handling

## Uses

### Direct Use
The AGI Validator is designed for:
- Verifying factual claims in research and academia
- Validating knowledge-based assertions in AGI systems
- Analyzing evidence chains for logical consistency
- Cross-domain knowledge verification
- Educational content validation

### Downstream Use
- Integration with knowledge management systems
- Fact-checking platforms
- Research assistant tools
- Educational technology platforms
- AI safety verification systems

### Out-of-Scope Use
- Making subjective judgments
- Personal opinion validation
- Legal decision making
- Medical diagnosis
- Real-time critical systems

## How to Get Started

```python
from agi_validator import EnhancedAGIValidator, UniversalClaim

# Initialize validator
validator = EnhancedAGIValidator(mcp_enabled=True)

# Create knowledge claim
claim = UniversalClaim(
    claim_id="climate_change_001",
    content="Human activity is the primary driver of recent climate change",
    reasoning_modes=["bayesian", "causal"],
    sub_domains=["science", "social_science"]
)

# Add evidence
claim.evidence_chain.append(
    Evidence(
        evidence_id="ipcc_ar6",
        strength=0.95,
        reliability=0.9,
        source_quality=0.95,
        domain="science"
    )
)

# Validate claim
validation_report = await validator.validate_knowledge_claim(claim)
print(validation_report)
```

## Technical Specifications

### System Architecture
- **Core Components:**
  - Evidence Analysis Engine
  - Reasoning Mode Evaluator (Deductive/Inductive/Abductive/Bayesian/Causal)
  - Multi-Consensus Protocol (mCP) Interface
  - Real-time Data Integrator
  - Domain Constraint Handler

- **Analytical Capabilities:**
  - Dynamic validation threshold calculation
  - Metacognitive bias detection
  - Evidence quality scoring
  - Domain-specific rule application
  - Contradiction detection

### Compute Infrastructure
- **Hardware Requirements:**
  - Minimum: 4GB RAM, 2-core CPU
  - Recommended: 8GB+ RAM, 4+ core CPU
- **Software Dependencies:**
  - Python 3.10+
  - aiohttp
  - numpy
  - FastAPI (for web interface)
  - Uvicorn (ASGI server)

## Evaluation

### Testing Methodology
- Validation against curated test cases across domains
- Consistency checks with known facts
- Stress testing with contradictory evidence
- Performance benchmarking
- Error recovery testing

### Key Metrics
- **Claim Validity Score:** 0.0-1.0 scale
- **Evidence Quality Score:** Composite metric
- **Reasoning Coherence:** Logical consistency measure
- **System Reliability:** Uptime and error rate
- **Processing Time:** Average validation duration

## Environmental Impact
- **Carbon Efficiency:** Designed for minimal compute footprint
- **Optimization:** Asynchronous processing reduces energy consumption
- **Scaling:** Horizontal scaling capability minimizes resource waste
- **Estimated Energy Usage:** < 0.001 kWh per validation

## Citation
```bibtex
@software{AGI_Validator, veil engine technology
  author = {thegift_thecurse},
  title = {Advanced AGI Validation System} Framework,
  year = {2025},

}
```

## Model Card Contact
upgraedd@pm.me
```