IntelligentEstate/ADV_AGI_FRAME

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

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

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

}

Model Card Contact

[email protected] ```