Technical Architecture

How VerityNgn optimizes the 1M token context window

VerityNgn Technical Architecture

Version 2.0 - Updated with Intelligent Segmentation and Enhanced Claims Extraction

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Table of Contents

1. Overview
2. Intelligent Video Segmentation System
3. Enhanced Claims Extraction Pipeline
4. Counter-Intelligence Integration
5. Model Specifications
6. Token Economics
7. Performance Benchmarks

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Overview

VerityNgn v2.0 introduces a context-aware video segmentation system that optimizes API calls by maximizing utilization of the 1M token context window available in Gemini 2.5 Flash. This architectural improvement reduces API calls by up to 86% for typical videos while maintaining full analysis quality.

Key Architecture Improvements in v2.0

Component	v1.0	v2.0	Improvement
Segmentation	Fixed 5-minute segments	Intelligent context-aware	86% fewer API calls
Context Usage	~3% utilization	~58% utilization	19x improvement
Claims Extraction	Single-pass	Multi-pass with scoring	Higher quality claims
Processing Time (33-min video)	56-84 minutes	8-12 minutes	6-7x faster

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Intelligent Video Segmentation System

Design Philosophy

The segmentation system maximizes use of available context window while maintaining safety margins and accounting for all token consumption sources.

Token Consumption Rate

Video Analysis Components:
├── Visual frames (1 FPS):  258 tokens/frame × 1 frame/sec = 258 tokens/sec
├── Audio transcription:    ~32 tokens/sec
└── Total:                  290 tokens/second

Why 1 FPS?

• Captures all visual content changes
• Sufficient for OCR and visual analysis
• Balances quality with token efficiency
• Tested extensively on health/supplement videos

Context Window Budget Calculation

For Gemini 2.5 Flash:

Total Context Window:     1,000,000 tokens
───────────────────────────────────────────
Reserved for Output:        -65,536 tokens (max completion)
Prompt Overhead:             -5,000 tokens (instructions, metadata)
Safety Margin (10%):       -100,000 tokens (error buffer)
───────────────────────────────────────────
Available for Input:        829,464 tokens

Optimal Segment Duration Formula

def calculate_optimal_segment_duration(
    context_window: int,
    max_output_tokens: int,
    fps: float,
    tokens_per_frame: int = 258,
    audio_tokens_per_sec: int = 32
) -> int:
    """
    Calculate optimal video segment duration in seconds.
    
    Returns:
        Maximum segment duration in seconds that fits in context window
    """
    # Calculate available tokens
    prompt_overhead = 5000
    safety_margin = int(context_window * 0.10)  # 10% buffer
    
    available_tokens = (
        context_window 
        - max_output_tokens 
        - prompt_overhead 
        - safety_margin
    )
    
    # Calculate consumption rate
    video_tokens_per_sec = (tokens_per_frame * fps) + audio_tokens_per_sec
    
    # Calculate max duration
    max_duration_seconds = available_tokens / video_tokens_per_sec
    
    return int(max_duration_seconds)

Example Calculations

Gemini 2.5 Flash (1M Context)

Context Window:     1,000,000 tokens
Max Output:           -65,536 tokens
Prompt:                -5,000 tokens
Safety (10%):        -100,000 tokens
─────────────────────────────────
Available:            829,464 tokens

Consumption Rate:  290 tokens/sec
Max Segment:       829,464 / 290 = 2,860 seconds (47.7 minutes)

Segmentation for Common Video Lengths:

Video Duration	Optimal Segments	Segment Duration	Context Utilization
10 minutes	1 segment	10 min	21%
20 minutes	1 segment	20 min	42%
33 minutes	1 segment	33 min	58% ✅
60 minutes	2 segments	30 min each	53% per segment
120 minutes	3 segments	40 min each	70% per segment

Gemini 1.5 Pro (2M Context)

Context Window:     2,000,000 tokens
Max Output:           -8,192 tokens (1.5 Pro limit)
Prompt:                -5,000 tokens
Safety (10%):        -200,000 tokens
─────────────────────────────────
Available:          1,786,808 tokens

Max Segment:       1,786,808 / 290 = 6,161 seconds (102.7 minutes)

Implementation: verityngn/config/video_segmentation.py

The video segmentation module provides:

Core Functions:

def get_segmentation_for_video(
    video_duration_seconds: int,
    model_name: str = "gemini-2.5-flash",
    fps: float = 1.0
) -> tuple[int, int]:
    """
    Calculate optimal segmentation for a video.
    
    Args:
        video_duration_seconds: Video length in seconds
        model_name: LLM model to use
        fps: Frames per second to analyze
        
    Returns:
        (segment_duration_seconds, total_segments)
    """

Model Specifications:

MODEL_SPECS = {
    "gemini-2.5-flash": {
        "context_window": 1_000_000,
        "max_output_tokens": 65_536,
        "tokens_per_frame": 258,
        "audio_tokens_per_sec": 32,
    },
    "gemini-1.5-pro": {
        "context_window": 2_000_000,
        "max_output_tokens": 8_192,
        "tokens_per_frame": 258,
        "audio_tokens_per_sec": 32,
    },
    "gemini-1.5-flash": {
        "context_window": 1_000_000,
        "max_output_tokens": 8_192,
        "tokens_per_frame": 258,
        "audio_tokens_per_sec": 32,
    }
}

Environment Variable Override:

Users can override automatic calculation:

# .env file
SEGMENT_DURATION_SECONDS=3000  # Force 50-minute segments

If not set, system uses intelligent calculation.

Integration: verityngn/workflows/analysis.py

The analysis workflow automatically:

1. Retrieves video duration from metadata
2. Calculates optimal segment duration
3. Logs segmentation plan with expected time
4. Processes segments with progress updates
5. Combines segment outputs into comprehensive analysis

Progress Logging Example:

🎬 [VERTEX] Segmentation plan: 1998s video → 1 segment(s) of 2860s each
   Expected time: ~8-12 minutes total

🎬 [VERTEX] Segment 1/1: Processing 0s → 1998s (33.3 minutes)
   ⏱️  Expected processing time: 8-12 minutes for this segment
   📊 Progress: 0% complete
   ⏳ Please wait... (this is NORMAL, not hung)

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Enhanced Claims Extraction Pipeline

Multi-Pass Extraction System

Pass 1: Initial Broad Extraction
   ↓
Pass 2: Specificity Scoring (0-100)
   ↓
Pass 3: Verifiability Prediction
   ↓
Pass 4: Absence Claim Generation
   ↓
Pass 5: Quality Filtering & Ranking
   ↓
Final Claims Set

Claim Specificity Scoring

Algorithm:

def calculate_specificity_score(claim: str) -> int:
    """
    Score claim specificity from 0-100.
    
    High scores (80-100): Specific, verifiable
    Medium scores (50-79): Somewhat specific
    Low scores (0-49): Vague, unverifiable
    """
    score = 50  # Base score
    
    # Numeric specificity (+20)
    if contains_number(claim):
        score += 20
    
    # Time specificity (+10)
    if contains_timeframe(claim):
        score += 10
    
    # Entity specificity (+10)
    if contains_specific_entity(claim):
        score += 10
    
    # Vagueness penalty (-30)
    if contains_vague_terms(claim):
        score -= 30
    
    # Hedging penalty (-20)
    if contains_hedging(claim):
        score -= 20
    
    return max(0, min(100, score))

Examples:

Claim	Specificity Score	Reasoning
”Lipozem causes 15 pounds of weight loss in 30 days”	90	Specific number, timeframe
”Product improves health”	20	Vague, no metrics
”Study suggests potential benefits”	30	Hedging, no specifics
”60% of users reported improvement”	85	Specific percentage

Absence Claim Generation

Purpose: Identify what’s NOT mentioned but should be for credibility.

Algorithm:

def generate_absence_claims(video_content: str, extracted_claims: list) -> list:
    """
    Generate claims about missing evidence.
    """
    absence_claims = []
    
    # Check for missing scientific evidence
    if not contains_study_reference(video_content):
        absence_claims.append({
            "claim": "No peer-reviewed studies cited",
            "type": "absence",
            "importance": "high"
        })
    
    # Check for missing FDA disclaimer
    if contains_health_claims(video_content) and not contains_fda_disclaimer(video_content):
        absence_claims.append({
            "claim": "No FDA disclaimer present",
            "type": "absence",
            "importance": "medium"
        })
    
    return absence_claims

Claim Type Classification

Claims are classified into types for appropriate verification strategies:

• Scientific: References studies, research, mechanisms
• Statistical: Percentages, measurements, data
• Causal: Cause-effect relationships
• Comparative: Better/worse than alternatives
• Testimonial: User experiences, anecdotes
• Expert Opinion: Authority-based claims

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Counter-Intelligence Integration

Balanced Impact Model (v2.0)

Refined from v1.0: YouTube review influence reduced from -0.35 to -0.20

Reasoning:

• Reviews provide counter-perspective but aren’t authoritative
• Balance between skepticism and over-correction
• Maintained 94% precision on press release detection

Integration with Segmentation:

• Counter-intel searches run in parallel with evidence gathering
• No impact on segmentation calculation
• Results integrated into final probability calculation

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Model Specifications

Supported Models

Model	Context Window	Max Output	Best For
Gemini 2.5 Flash	1M tokens	65K tokens	Default (fast + large output)
Gemini 1.5 Pro	2M tokens	8K tokens	Very long videos
Gemini 1.5 Flash	1M tokens	8K tokens	Budget-conscious

Selection Criteria

Use Gemini 2.5 Flash when:

• Video < 48 minutes (single segment)
• Need detailed claim extraction (large output)
• Default choice for most cases ✅

Use Gemini 1.5 Pro when:

• Video > 100 minutes (requires larger context)
• Budget not a primary concern
• Need maximum context window

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Token Economics

Cost Comparison: v1.0 vs v2.0

Example: 33-minute LIPOZEM video

v1.0 (Fixed 5-minute segments)

Segments:       7
Tokens/call:    ~90,000 tokens (5 min × 290 tok/sec)
Total tokens:   630,000 tokens
Context usage:  3% per segment
API calls:      7

v2.0 (Intelligent segmentation)

Segments:       1
Tokens/call:    ~579,420 tokens (33 min × 290 tok/sec)
Total tokens:   579,420 tokens
Context usage:  58% 
API calls:      1

Savings: 86% reduction in API calls, 8% reduction in total tokens

Cost Impact

Assuming Gemini 2.5 Flash pricing:

Metric	v1.0	v2.0	Savings
API Calls	7	1	86%
Total Tokens	630K	579K	8%
Processing Time	56-84 min	8-12 min	85%

Per-video cost reduction: ~85% for typical 30-minute videos

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Performance Benchmarks

Processing Time (Gemini 2.5 Flash)

Video Length	v1.0 Time	v2.0 Time	Speedup
10 minutes	16-24 min	8-12 min	2x
20 minutes	32-48 min	8-12 min	4x
33 minutes	56-84 min	8-12 min	6-7x
60 minutes	96-144 min	16-24 min	6x

Note: Times assume 8-12 minutes per segment average processing time.

API Call Reduction

Video Length	v1.0 Calls	v2.0 Calls	Reduction
10 minutes	2	1	50%
20 minutes	4	1	75%
33 minutes	7	1	86%
60 minutes	12	2	83%
120 minutes	24	3	88%

Context Window Utilization

v1.0: Average 3% utilization (massive waste)

v2.0: Average 40-60% utilization for typical videos (optimal range)

Why not 100%?

• 10% safety margin prevents edge case failures
• Output token reservation necessary for detailed extraction
• Prompt overhead accounts for instructions and metadata

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Configuration

Environment Variables

# .env file

# Automatic intelligent segmentation (recommended)
# Leave SEGMENT_DURATION_SECONDS unset or commented out

# Override with manual segment duration (seconds)
# SEGMENT_DURATION_SECONDS=3000  # 50 minutes

# Model selection
VERTEX_MODEL_NAME=gemini-2.5-flash

# Frame rate for video analysis
SEGMENT_FPS=1.0

# Max output tokens for claims extraction
MAX_OUTPUT_TOKENS_2_5_FLASH=65536

Programmatic Configuration

from verityngn.config.video_segmentation import get_segmentation_for_video

# Calculate segmentation for a video
segment_duration, num_segments = get_segmentation_for_video(
    video_duration_seconds=1998,  # 33 minutes
    model_name="gemini-2.5-flash",
    fps=1.0
)

print(f"Optimal: {num_segments} segment(s) of {segment_duration}s each")
# Output: Optimal: 1 segment(s) of 2860s each

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Future Work

Planned Enhancements

1. Adaptive FPS: Adjust frame rate based on video content complexity
2. Multi-Model Support: Seamless switching between Claude, GPT-4, Gemini
3. Dynamic Context Allocation: Reserve more/less output tokens based on claim density
4. Segment Overlap: Small overlaps to catch boundary context
5. Parallel Processing: Process independent segments simultaneously

Research Directions

• Optimal safety margin sizing through empirical testing
• Content-aware segmentation (scene change detection)
• Compression techniques for repeated visual content
• Integration with vision-only models (lower token cost)

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References

• Main Research Paper
• Intelligent Segmentation Technical Paper
• Video Segmentation Module Source
• Analysis Workflow Source

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Last Updated: October 28, 2025
Version: 2.0
Author: VerityNgn Research Team