NEXUS AI Engine | Verity Systems

The Name

What Does NEXUS Stand For?

Each letter represents a core pillar of our verification intelligence

N

Neural

Deep learning architectures with transformer attention mechanisms

E

Expert

Domain-specialized models trained on fact-checking datasets

X

Cross-validation

Multi-source verification with consensus building

U

Unified

Integrated pipeline combining all verification signals

S

System

End-to-end architecture with real-time processing

Architecture

How NEXUS Works

Six interconnected modules working in harmony

🧠

Neural Ensemble

Multiple specialized neural networks vote on verdicts, combining transformer encoders, attention mechanisms, and uncertainty quantification for robust predictions.

🎯

Meta-Learning

Learns to learn from new claim types with few examples. Adapts to emerging topics, evolving language patterns, and novel misinformation tactics in real-time.

⚖️

Adaptive Weighting

Dynamically adjusts provider weights based on domain, recency, and historical accuracy. Political claims weight fact-checkers higher; scientific claims favor journals.

📊

Confidence Calibration

Temperature scaling and Monte Carlo dropout ensure our confidence scores match real-world accuracy. 80% confidence = 80% correct, verified against ground truth.

🔄

Continual Learning

Experience replay and elastic weight consolidation prevent catastrophic forgetting while learning new patterns. The system never forgets what it learned.

💡

Explainability

SHAP values, attention visualization, and natural language explanations show exactly why each verdict was reached. Full transparency, no black boxes.

768

Hidden Dimensions

12

Attention Heads

50+

Data Providers

20

MC Dropout Samples

Capabilities

What Makes NEXUS Special

Advanced capabilities that set us apart

01

Few-Shot Learning

Verify new claim types with as few as 5 examples. MAML-based optimization enables rapid adaptation to emerging topics.

02

Uncertainty Quantification

Monte Carlo dropout with 20 forward passes provides epistemic uncertainty estimates. Know when the model is confident vs. unsure.

03

Knowledge Distillation

Large language model knowledge compressed into efficient runtime models. GPT-4 level reasoning at millisecond latency.

04

Adversarial Robustness

Trained against paraphrase attacks, negation injection, and context manipulation. Resistant to attempts to fool the system.

05

Multi-Modal Verification

Text, images, PDFs, and URLs processed through unified embeddings. Cross-reference claims across media types.

Self-Improvement

Continuous Learning Loop

NEXUS gets smarter with every verification

📥

Ingest Data

→

🔍

Verify Claim

→

📊

Collect Feedback

→

🧠

Update Weights

→

📈

Improve Model

Training Data

What Feeds NEXUS

Diverse, high-quality datasets from trusted sources

📰

Fact-Check Orgs

Snopes, PolitiFact, FactCheck.org

🎓

Academic Papers

LIAR, FEVER, MultiFC datasets

📚

Encyclopedias

Wikipedia, Wikidata, Britannica

🔬

Scientific Journals

PubMed, Semantic Scholar, arXiv

🏛️

Government Data

CDC, WHO, NASA, Census

📊

Statistics

World Bank, FRED, UN Data

🌐

News Archives

Reuters, AP, GDELT

🤖

Synthetic Data

Paraphrases, Augmentations

Developer Access

Use NEXUS in Your Apps

Full API access to our verification engine

                nexus_verify.py
                Python
            

import verity

# Initialize with your API key
client = verity.Client(api_key="your_key")

# Verify a claim with NEXUS
result = client.verify(
    claim="The Earth is approximately 4.5 billion years old",
    engine="nexus",  # Use NEXUS explicitly
    options={
        "depth": "deep",           # Full analysis
        "explain": True,           # Get explanations
        "confidence_interval": True # Uncertainty bounds
    }
)

print(f"Verdict: {result.verdict}")        # TRUE
print(f"Confidence: {result.confidence}%") # 94.7%
print(f"Uncertainty: ±{result.uncertainty}%") # ±2.1%
            

NEXUS: The Brain Behind Verity