Methodology

The science behind multimodal verification and probabilistic scoring

VerityNgn Methodology Documentation

Version 1.0 | Last Updated: October 23, 2025

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Executive Summary

VerityNgn is a multimodal AI-powered video verification system that analyzes YouTube videos to assess the truthfulness of claims made within them. The system combines cutting-edge multimodal LLM analysis, counter-intelligence techniques, and probabilistic reasoning to generate comprehensive truthfulness reports.

Core Innovation: VerityNgn is the first system to combine:

1. Frame-by-frame multimodal video analysis (1 FPS sampling)
2. YouTube counter-intelligence (analyzing review videos for contradictory evidence)
3. Press release bias detection and penalty system
4. Probabilistic truthfulness assessment with evidence weighting

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Table of Contents

1. System Architecture
2. Multimodal Analysis Pipeline
3. Claims Extraction Methodology
4. Counter-Intelligence System
5. Probability Distribution Model
6. Evidence Classification & Weighting
7. Verification Algorithm
8. Report Generation
9. Limitations & Future Work

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1. System Architecture

Overview

VerityNgn follows a pipeline architecture with six main stages:

┌─────────────────────────────────────────────────────┐
│  Stage 1: Video Download & Preprocessing            │
│  - YouTube URL validation                            │
│  - Video metadata extraction                         │
│  - Transcript extraction                             │
└────────────┬────────────────────────────────────────┘
             │
             ▼
┌─────────────────────────────────────────────────────┐
│  Stage 2: Multimodal Analysis                        │
│  - Frame-by-frame video analysis (1 FPS)            │
│  - Audio transcription with timestamps              │
│  - OCR for visual text extraction                   │
│  - Claims extraction (5-15 claims per video)        │
└────────────┬────────────────────────────────────────┘
             │
             ▼
┌─────────────────────────────────────────────────────┐
│  Stage 3: Counter-Intelligence Gathering            │
│  - YouTube review search                             │
│  - Contradictory evidence detection                  │
│  - Press release identification                      │
└────────────┬────────────────────────────────────────┘
             │
             ▼
┌─────────────────────────────────────────────────────┐
│  Stage 4: Claims Verification                        │
│  - Web search for supporting evidence               │
│  - Evidence classification & weighting              │
│  - Source credibility assessment                    │
└────────────┬────────────────────────────────────────┘
             │
             ▼
┌─────────────────────────────────────────────────────┐
│  Stage 5: Probability Calculation                    │
│  - Multi-factor probability distribution            │
│  - Counter-intelligence adjustments                  │
│  - Evidence quality boosts                           │
│  - Normalization & mapping to verdicts              │
└────────────┬────────────────────────────────────────┘
             │
             ▼
┌─────────────────────────────────────────────────────┐
│  Stage 6: Report Generation                          │
│  - HTML, Markdown, and JSON formats                 │
│  - Evidence compilation                              │
│  - Detailed explanations                             │
│  - Source attribution                                │
└─────────────────────────────────────────────────────┘

Technology Stack

• Multimodal LLM: Google Gemini 2.5 Flash (via Vertex AI)
• Context Window: 64K tokens for comprehensive analysis
• Video Processing: yt-dlp for video download and metadata
• Web Search: Google Custom Search API
• Framework: LangGraph for workflow orchestration
• Storage: Local filesystem or Google Cloud Storage

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2. Multimodal Analysis Pipeline

Video Processing Strategy

VerityNgn uses an aggressive frame-sampling approach:

SAMPLING_RATE = 1 FPS  # 1 frame per second
TARGET_CLAIMS = 3 per minute of video
CONTEXT_WINDOW = 64K tokens (Gemini 2.5 Flash)

Frame-by-Frame Analysis

For each frame, the system analyzes:

1. Visual Content:

   • On-screen text and graphics
   • Demonstrations and visual evidence
   • Charts, graphs, and data visualizations
   • Product displays or examples

2. Audio Track:

   • Spoken statements with timestamps
   • Speaker identification
   • Tone and delivery analysis

3. Metadata:

   • Video title and description
   • Channel information
   • Upload date and view count
   • Comments and engagement metrics

CRAAP Criteria Integration

All claims are evaluated using the CRAAP framework:

• Currency: When was the information published?
• Relevance: Is it relevant to the video’s main message?
• Authority: What credentials or expertise is claimed?
• Accuracy: Can it be verified with external sources?
• Purpose: Is it promotional, educational, or persuasive?

Prompt Engineering

The system uses carefully crafted prompts to ensure high-quality claim extraction:

CRITICAL INSTRUCTIONS FOR MULTIMODAL VIDEO ANALYSIS:
- Analyze this video with MAXIMUM detail at 1 FRAME PER SECOND sampling rate
- Extract ALL factual claims, statements, assertions from ACTUAL VIDEO CONTENT
- Focus on SPOKEN WORDS, VISUAL TEXT, ON-SCREEN GRAPHICS, and DEMONSTRATIONS
- Ignore background music, decorative elements, or irrelevant visuals
- Extract EXACTLY 5-15 specific, verifiable claims

Claim Mix Requirements:

• 70% Scientific & Verifiable Claims (studies, statistics, product effectiveness)
• 10% Speaker Credibility Claims (credentials, affiliations, experience)
• 20% Other Verifiable Claims (dates, locations, specific statements)

Output Format

Claims are extracted in structured JSON:

{
  "claim_text": "Dr. X has 20 years of experience in endocrinology",
  "timestamp": "02:15",
  "speaker": "Dr. X (Narrator)",
  "source_type": "spoken",
  "initial_assessment": "Verifiable credential claim requiring external verification"
}

---

3. Claims Extraction Methodology

Claim Quality Criteria

Each extracted claim must meet these requirements:

1. Specificity: Concrete, not vague or general
2. Verifiability: Can be checked against external sources
3. Relevance: Central to the video’s message
4. Factuality: Stated as fact, not opinion
5. Significance: Meaningful for truthfulness assessment

Claim Types

Primary Claims (70%):

• Study results: “Study X found Y outcome”
• Statistics: “75% of patients experienced Z”
• Product effectiveness: “Product causes W in N days”
• Scientific findings: “Research shows…”

Secondary Claims (10%):

• Educational credentials: “Dr. X studied at Y”
• Professional experience: “X years in field”
• Institutional affiliations: “Works at Hospital Y”
• Awards and recognitions

Other Claims (20%):

• Historical facts
• Geographic information
• Temporal data
• Specific events or occurrences

Anti-Patterns (Avoided)

The system explicitly avoids:

• Vague motivational statements
• General health advice without specifics
• Obvious facts not requiring verification
• Micro-claims too granular for meaningful assessment
• Subjective opinions without factual basis

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4. Counter-Intelligence System

Overview

VerityNgn’s counter-intelligence system is designed to detect and weight contradictory evidence that challenges claims made in the analyzed video. This is one of the system’s most innovative features.

Two-Pronged Approach

A. Press Release Detection

Purpose: Identify self-promotional content that lacks independent validation.

Detection Method:

PRESS_RELEASE_INDICATORS = [
    'prnewswire.com', 'businesswire.com', 'globenewswire.com',
    'prnewswire', 'marketwired', 'accesswire', 'prweb.com',
    'press release', 'newswire', 'press statement'
]

Bias Application:

• Probability Adjustment: -0.4 (significant negative bias)
• Rationale: Press releases are promotional, not independent verification
• Distribution Impact:
  • Reduce TRUE by 60% of adjustment
  • Increase FALSE by 40% of adjustment
  • Increase UNCERTAIN by 40% of adjustment

Example:

Claim: "Product X causes 15 pounds of weight loss"
Evidence: 5 press releases from product manufacturer

Result: TRUE probability reduced by 24% (0.4 * 0.6)
        FALSE probability increased by 16% (0.4 * 0.4)
        UNCERTAIN increased by 16% (0.4 * 0.4)

B. YouTube Counter-Intelligence

Purpose: Find and analyze review videos that contradict claims in the original video.

Search Strategy:

COUNTER_INTEL_QUERIES = [
    "{video_title} scam review",
    "{video_title} fake exposed",
    "{video_title} debunk analysis",
    "{main_keywords} + warning review",
    "{product_name} doesn't work"
]

Transcript Analysis:

The system analyzes transcripts of review videos for:

1. Counter-Signals:

   • ‘scam’, ‘fake’, ‘fraud’, ‘lie’, ‘misleading’
   • ‘doesn’t work’, ‘waste of money’, ‘no results’
   • ‘red flags’, ‘warning’, ‘beware’, ‘avoid’
   • ‘overpriced’, ‘overhyped’, ‘disappointing’
   • ‘fabricated’, ‘deceptive’, ‘predatory’, ‘exposed’

2. Supporting Signals:

   • ‘works’, ‘effective’, ‘results’, ‘recommend’
   • ‘good’, ‘helps’, ‘success’, ‘positive’
   • ‘beneficial’, ‘worth it’, ‘legit’

Stance Determination:

counter_ratio = counter_signals / (counter_signals + supporting_signals)

if counter_ratio > 0.7:
    stance = 'counter'
    confidence = min(0.95, 0.6 + (counter_ratio - 0.7) * 1.17)
elif counter_ratio < 0.3:
    stance = 'supporting'
    confidence = min(0.95, 0.6 + (0.3 - counter_ratio) * 1.17)
else:
    stance = 'neutral'
    confidence = 0.5

Credibility Weighting:

Counter-intelligence evidence is weighted by:

• View Count: Videos with 100K+ views = stronger signal
• Channel Credibility: Established channels weighted higher
• Video Age: Recent videos weighted higher
• Engagement: Like ratio and comment sentiment

Probability Impact:

YouTube counter-intelligence has reduced impact compared to earlier versions:

# Enhanced (balanced) approach:
youtube_impact = min(0.20, youtube_counter_power * 0.08)
enhanced_dist["FALSE"] = min(0.85, base_false + youtube_impact)

# Reduction rationale: Avoid over-aggressive FALSE bias
# Previous: max 0.35 impact, max 0.9 FALSE
# Current: max 0.20 impact, max 0.85 FALSE

Counter-Intelligence in Action

Example Case:

Original Video: "Amazing weight loss supplement - lose 15 pounds!"

Counter-Intelligence Found:
1. "Product X Scam Exposed" (450K views) - Counter stance (90% confidence)
2. "I Tried Product X - My Honest Review" (120K views) - Counter stance (85% confidence)
3. "Product X Doesn't Work - Warning" (75K views) - Counter stance (80% confidence)

Impact:
- Base FALSE probability: 30%
- YouTube CI adjustment: +12% (0.12 based on reach and confidence)
- Final FALSE probability: 42%

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5. Probability Distribution Model

Three-State Model

VerityNgn uses a probabilistic approach rather than binary true/false:

P(TRUE) + P(FALSE) + P(UNCERTAIN) = 1.0

Base Probability Initialization

base_dist = {
    "TRUE": 0.3,      # Starting assumption: neutral
    "FALSE": 0.3,      # Equal weight to false
    "UNCERTAIN": 0.4   # Highest initial uncertainty
}

Enhancement Factors

The base distribution is adjusted based on five key factors:

Factor 1: Evidence Coverage (10-30% boost)

coverage_score = evidence_count / (claim_word_count / 5)
if coverage_score > 1.0:
    boost = min(0.3, coverage_score * 0.15)
    enhanced_dist["TRUE"] += boost

Rationale: More evidence per claim indicates stronger validation.

Factor 2: Independent Source Ratio (10-25% boost)

independent_ratio = independent_sources / total_sources
if independent_ratio > 0.5:
    boost = min(0.25, (independent_ratio - 0.5) * 0.5)
    enhanced_dist["TRUE"] += boost

Rationale: Independent sources provide unbiased validation.

Factor 3: Scientific Evidence (up to 40% boost)

science_weight = min(0.4, scientific_power * 0.2)
enhanced_dist["TRUE"] += science_weight

Rationale: Scientific sources (journals, academic institutions) carry highest weight.

Factor 4: YouTube Counter-Intelligence (-20% impact)

youtube_impact = min(0.20, youtube_counter_power * 0.08)
enhanced_dist["FALSE"] += youtube_impact

Rationale: Contradictory reviews reduce claim trustworthiness.

Factor 5: Press Release Penalty (-40% impact)

self_ref_count = count_self_referential_sources()
penalty = min(0.4, self_ref_count * 0.15)
enhanced_dist["FALSE"] += penalty

Rationale: Self-promotional content is inherently biased.

Normalization

After all adjustments, probabilities are normalized:

total = sum(enhanced_dist.values())
if total > 0:
    enhanced_dist = {k: v / total for k, v in enhanced_dist.items()}

# Ensure minimum thresholds
for key in ["TRUE", "FALSE", "UNCERTAIN"]:
    enhanced_dist[key] = max(0.001, enhanced_dist[key])

Verdict Mapping

The continuous probabilities are mapped to human-readable verdicts:

TRUE% = P(TRUE) * 100
FALSE% = P(FALSE) * 100
UNCERTAIN% = P(UNCERTAIN) * 100

if TRUE \> 70 and FALSE \< 10:
    verdict = "HIGHLY_LIKELY_TRUE"
elif (TRUE + UNCERTAIN) \> 65 and FALSE \< 35:
    verdict = "LIKELY_TRUE"
elif TRUE \> 40 and FALSE \< 35:
    verdict = "LEANING_TRUE"
elif abs(TRUE - FALSE) \< 10:
    verdict = "UNCERTAIN"
elif FALSE \> 35 and TRUE \< 30:
    verdict = "LEANING_FALSE"
elif (FALSE + UNCERTAIN) \> 65 and TRUE \< 35:
    verdict = "LIKELY_FALSE"
elif FALSE \> 75:
    verdict = "HIGHLY_LIKELY_FALSE"

Rationale for Thresholds:

• 65% threshold for “likely” verdicts (relaxed from 70% to avoid over-conservatism)
• 70% threshold for “highly likely” verdicts
• Combination with UNCERTAIN allows for nuanced assessment

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6. Evidence Classification & Weighting

Source Categories

Evidence is classified into six categories, each with distinct validation power:

1. Scientific Sources (Highest Weight: 2.5-4.0)

Characteristics:

• Peer-reviewed journals
• Academic institutions (.edu domains)
• Research papers and studies
• Government research agencies

Examples:

• PubMed/NCBI publications
• Journal articles (Nature, Science, NEJM)
• University research papers
• NIH/CDC reports

Validation Power: 2.5 - 4.0 (highest)

2. Independent Sources (High Weight: 1.5-2.0)

Characteristics:

• Reputable news organizations
• Fact-checking organizations
• Independent analysis
• No financial stake in outcome

Examples:

• Reuters, AP News, BBC
• FactCheck.org, Snopes, PolitiFact
• Independent medical websites (Mayo Clinic, WebMD)

Validation Power: 1.5 - 2.0

3. Government Sources (High Weight: 1.5-2.5)

Characteristics:

• .gov domains
• Official government publications
• Regulatory agency reports

Examples:

• FDA reports
• CDC guidelines
• NIH publications
• FTC consumer alerts

Validation Power: 1.5 - 2.5

4. Press Releases (Negative Weight: -0.5 to -1.0)

Characteristics:

• Self-promotional content
• Newswire services
• Company press statements
• Marketing materials

Examples:

• PR Newswire articles
• Business Wire releases
• Company blog posts
• Marketing pages

Validation Power: -0.5 to -1.0 (penalized)

5. YouTube Counter-Intelligence (Variable: 0.5-2.0)

Characteristics:

• Review videos
• Debunking content
• Investigation videos
• Consumer experiences

Weight Determined By:

• View count (100K+ = higher weight)
• Channel credibility
• Stance confidence
• Counter-signal strength

Validation Power: 0.5 - 2.0 (variable)

6. Other Web Sources (Standard Weight: 1.0)

Characteristics:

• General web pages
• Blogs
• Forums
• Social media

Validation Power: 1.0 (baseline)

Self-Referential Detection

The system identifies “self-referential” sources that cite the original video or its creators:

def is_self_referential(source_url, source_text, video_title, video_channel):
    # Check if source cites the original video
    if video_title.lower() in source_text.lower():
        return True
    if video_channel.lower() in source_text.lower():
        return True
    if source_url contains video_domain:
        return True
    return False

Self-referential sources are:

• Separated into press_releases category
• Given negative validation power
• Excluded from supporting evidence counts

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7. Verification Algorithm

Claim Verification Process

For each extracted claim, the system follows this process:

1. Generate search queries
   ├─ Fact-check queries: "{claim} fact check"
   ├─ Scientific queries: "{claim} study research"
   ├─ General queries: exact claim text
   └─ Debunk queries: "{claim} debunk false"

2. Execute web searches
   ├─ Google Custom Search API
   ├─ Retrieve top 10 results per query
   └─ Extract URL, title, snippet

3. Classify evidence
   ├─ Identify source category
   ├─ Assign validation power
   ├─ Detect self-referential content
   └─ Determine supporting vs contradicting

4. Calculate probability distribution
   ├─ Start with base distribution
   ├─ Apply evidence boosts
   ├─ Apply counter-intelligence penalties
   ├─ Normalize to sum = 1.0
   └─ Map to verdict category

5. Generate explanation
   ├─ Summarize evidence
   ├─ Explain probability factors
   ├─ Cite key sources
   └─ Provide confidence assessment

Multi-Factor Probability Calculation

The complete algorithm:

def calculate_enhanced_probability_distribution(
    base_dist: Dict[str, float],
    evidence_groups: Dict[str, List[Dict]]
) -> Tuple[Dict[str, float], List[str]]:
    
    enhanced_dist = base_dist.copy()
    modifications = []
    
    # Extract evidence by category
    scientific_evidence = evidence_groups["scientific"]
    independent_evidence = evidence_groups["independent"]
    press_releases = evidence_groups["press_releases"]
    youtube_counter = evidence_groups["youtube_reviews"]
    
    # Calculate validation powers
    scientific_power = sum(e['validation_power'] for e in scientific_evidence)
    independent_power = sum(e['validation_power'] for e in independent_evidence)
    youtube_counter_power = sum(e['validation_power'] for e in youtube_counter)
    
    # Factor 1: Evidence coverage
    total_evidence = len(scientific_evidence) + len(independent_evidence)
    claim_length = len(claim_text.split())
    coverage_score = total_evidence / (claim_length / 5)
    
    if coverage_score > 1.0:
        coverage_boost = min(0.3, coverage_score * 0.15)
        enhanced_dist["TRUE"] += coverage_boost
        modifications.append(f"Evidence coverage boost: +{coverage_boost:.2f}")
    
    # Factor 2: Independent source ratio
    if independent_power > 0:
        independent_ratio = independent_power / (independent_power + scientific_power + 0.01)
        if independent_ratio > 0.5:
            indie_boost = min(0.25, (independent_ratio - 0.5) * 0.5)
            enhanced_dist["TRUE"] += indie_boost
            modifications.append(f"Independent sources boost: +{indie_boost:.2f}")
    
    # Factor 3: Scientific evidence weighting
    if scientific_power > 0:
        science_weight = min(0.4, scientific_power * 0.2)
        enhanced_dist["TRUE"] += science_weight
        modifications.append(f"Scientific evidence boost: +{science_weight:.2f}")
    
    # Factor 4: YouTube counter-intelligence
    if youtube_counter_power > 0:
        youtube_impact = min(0.20, youtube_counter_power * 0.08)
        enhanced_dist["FALSE"] += youtube_impact
        modifications.append(f"YouTube CI penalty: +{youtube_impact:.2f} to FALSE")
    
    # Factor 5: Press release penalty
    self_ref_count = len([pr for pr in press_releases if pr.get('self_referential')])
    if self_ref_count > 0:
        penalty = min(0.4, self_ref_count * 0.15)
        enhanced_dist["FALSE"] += penalty
        modifications.append(f"Press release penalty: +{penalty:.2f} to FALSE")
    
    # Normalize
    total = sum(enhanced_dist.values())
    enhanced_dist = {k: v / total for k, v in enhanced_dist.items()}
    
    # Ensure minimums
    for key in ["TRUE", "FALSE", "UNCERTAIN"]:
        enhanced_dist[key] = max(0.001, enhanced_dist[key])
    
    return enhanced_dist, modifications

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8. Report Generation

Report Structure

Each report contains:

1. Executive Summary

   • Total claims analyzed
   • Verdict distribution (Highly Likely True, Likely False, etc.)
   • Overall truthfulness assessment

2. Video Information

   • Title, channel, duration
   • Upload date and view count
   • Video embed/thumbnail

3. Detailed Claims Analysis

   • Claim text with timestamp
   • Speaker identification
   • Verification verdict
   • Probability distribution
   • Detailed explanation
   • Source links

4. Evidence Summary

   • Categorized by source type
   • Validation power indicators
   • Quality metrics

5. Counter-Intelligence Section

   • Press releases identified
   • YouTube review analysis
   • Impact on truthfulness scores

Output Formats

HTML Report:

• Interactive source links (modal popups)
• Video embed
• Formatted tables
• Visual probability bars

Markdown Report:

• Clean, readable format
• Source citations with links
• Table-formatted claims analysis
• GitHub-compatible

JSON Report:

• Structured data format
• All probabilities and evidence
• Machine-readable
• API-friendly

Explanation Generation

For each claim, the system generates a human-readable explanation:

Template:

[VERDICT] based on [EVIDENCE_COUNT] sources ([SCIENTIFIC_COUNT] scientific, 
[INDEPENDENT_COUNT] independent). 

[COUNTER_INTEL_STATEMENT if applicable]

Key supporting evidence: [TOP_SOURCES]

Probability: TRUE: X%, FALSE: Y%, UNCERTAIN: Z%

Example:

LIKELY FALSE based on 8 sources (1 scientific, 3 independent). 

YOUTUBE COUNTER-INTELLIGENCE: Multiple review videos (450K+ views) contradict 
this claim, providing independent contradictory perspective.

Press release sources have reduced validation power due to self-promotional nature.

Key supporting evidence: [1] National Institutes of Health study shows no 
significant effect [2] Mayo Clinic states insufficient evidence.

Probability: TRUE: 15%, FALSE: 70%, UNCERTAIN: 15%

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9. Limitations & Future Work

Current Limitations

1. Language Support

• Current: English-only
• Impact: Cannot analyze non-English videos
• Mitigation: Rely on auto-translated transcripts (degraded quality)

2. Visual Understanding

• Current: Text-based analysis of visual content
• Impact: May miss subtle visual cues or demonstrations
• Mitigation: 1 FPS sampling captures most visual information

3. Context Understanding

• Current: Individual claim verification
• Impact: May miss broader narrative context
• Mitigation: Video analysis summary provides context

4. Search API Limitations

• Current: Dependent on Google Custom Search API
• Impact: Results limited by search algorithm bias
• Mitigation: Multiple query strategies, diverse sources

5. Real-Time Updates

• Current: Analysis is point-in-time
• Impact: New evidence published later not included
• Mitigation: Reports include generation timestamp

6. Subjective Claims

• Current: Best for factual, verifiable claims
• Impact: Opinion-based claims harder to verify
• Mitigation: Focus on CRAAP criteria, objective claims

Future Enhancements

1. Multi-Language Support

   • Integrate translation APIs
   • Support major world languages
   • Cross-language evidence validation

2. Enhanced Visual Analysis

   • Deeper computer vision integration
   • Object detection and recognition
   • Facial recognition for speaker verification

3. Real-Time Monitoring

   • Continuous evidence updates
   • Alert system for new contradictory evidence
   • Live verification scores

4. Community Validation

   • Crowd-sourced evidence submission
   • Expert reviewer network
   • Public challenge system

5. Automated Fact-Checking

   • Integration with fact-checking databases
   • Automated source verification
   • Claim similarity detection

6. Enhanced Counter-Intelligence

   • Social media analysis (Twitter, Reddit)
   • Forum scanning (specialized communities)
   • Academic preprint servers

7. Explainability Improvements

   • Visual probability explanations
   • Interactive evidence exploration
   • Confidence intervals on scores

---

Conclusion

VerityNgn represents a significant advancement in automated video verification technology. By combining multimodal AI analysis, sophisticated counter-intelligence techniques, and probabilistic reasoning, the system provides transparent, evidence-based truthfulness assessments.

Key Strengths:

• Comprehensive multimodal analysis
• Transparent probability calculations
• Counter-intelligence innovation
• Evidence-based reasoning
• Detailed explanations

Ethical Commitment:

• Transparent methodology
• Source attribution
• Probabilistic rather than binary verdicts
• Acknowledgment of uncertainty
• Open documentation

The system is designed to augment, not replace, human critical thinking. Users are encouraged to:

• Review the evidence themselves
• Consider the probability distributions
• Understand the limitations
• Make informed judgments

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For Technical Implementation Details, see:

• ARCHITECTURE.md - System architecture
• API_DOCUMENTATION.md - API reference
• DEPLOYMENT_GUIDE.md - Deployment instructions

For Research Papers, see:

• papers/verityngn_methodology.pdf - Academic paper
• papers/counter_intelligence_techniques.pdf - CI methods paper
• papers/probability_model.pdf - Mathematical foundations

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This methodology documentation is maintained as part of the VerityNgn open-source project. For updates and contributions, visit the GitHub repository.