verdict-weight 1.0.0

VERDICT WEIGHT™ — Context-Adaptive Multi-Source Confidence Synthesis Framework

VERDICT WEIGHT™

A Context-Adaptive Multi-Source Confidence Synthesis Framework for Autonomous AI Intelligence Systems

"Calibrated multi-source confidence scoring is not an optional feature of autonomous AI systems — it is a foundational architectural requirement." — VERDICT WEIGHT™ Paper, SSRN 6532658

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The Problem

Autonomous AI systems ingest intelligence from multiple heterogeneous sources and treat them as roughly equal. A rumor on a threat forum and a Mandiant primary incident report receive the same weight. A 30-day-old signal and a real-time alert are scored identically. A spoofed high-credibility source sails straight through.

That is not a UI problem. That is a systematic architectural vulnerability.

The Solution

VERDICT WEIGHT™ computes three complementary confidence outputs from four evidence streams:

Four Evidence Streams

Stream	Symbol	Description
Source Reliability	SR	Credibility of the originating source
Cross-Feed Corroboration	CC	Independent confirmation across feeds
Temporal Decay	TD	Recency of the intelligence signal
Historical Source Accuracy	HA	Empirical track record of the source

Three Output Components

Output	Symbol	Description
Signal Strength	SS	Confidence the signal is real (0.0–1.0)
Doubt Index	DI	Inter-stream disagreement (0.0–1.0)
Consequence Weight	CW	Actionability score after doubt adjustment (0.0–1.0)

Twelve Context Profiles

VERDICT WEIGHT™ ships with optimized weight profiles for twelve operational domains:

Domain	Profile
Cybersecurity (General)	Corroboration-dominant
Cybersecurity (APT)	Source reliability elevated
Cybersecurity (Zero-Day)	Temporal decay dominant
Cybersecurity (Disinformation)	Maximum corroboration, maximum doubt penalty
Healthcare (Diagnostic)	High doubt penalty, surfaces uncertainty
Healthcare (Drug Safety)	Highest doubt penalty in registry
Financial (Fraud)	Corroboration + recency co-dominant
Financial (Market)	Temporal decay dominant, fast decay
Defense Intelligence	Multi-source fusion, slow decay
Autonomous Vehicle	Sub-second decay, highest doubt penalty
Legal Evidence	Minimal decay, chain of custody weighted
Enterprise RAG	LLM retrieval confidence scoring

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Validated Results

Synthetic validation across N=1,000 controlled scenarios:

Metric	VERDICT WEIGHT™	Equal Weight	Single Source
Brier Score ↓	0.2077	0.2126	0.2561
AUC-ROC	0.7391	0.7398	0.6370
AUC-PR	0.7377	0.7374	0.6203
Adv. Suppression ↓	0.409	0.560	0.803

+18.9% Brier Score improvement and 49.1% adversarial intelligence suppression vs single-source baselines.

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Quick Start

from verdict_weight import VerdictWeight, ContextType

vw = VerdictWeight()

# Score a cybersecurity threat intel signal
result = vw.score(
    source_reliability=0.92,
    n_corroborating_sources=3,
    age_value=2.5,
    correct_predictions=45,
    total_predictions=50,
    context=ContextType.CYBERSECURITY_APT
)

print(result.action_tier)          # CRITICAL
print(result.consequence_weight)   # 0.8547
print(result.doubt_index)          # 0.0691
print(result.interpretation)       # Act immediately...
print(result.to_json())            # Full JSON output

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Installation

pip install verdict-weight

Or from source:

git clone https://github.com/Odingard/verdict-weight.git
cd verdict-weight
pip install -e .

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Repository Structure

verdict-weight/
├── verdict_weight/
│   ├── __init__.py          # Public API
│   ├── core.py              # VerdictWeight engine
│   ├── profiles.py          # 12 context weight profiles
│   ├── streams.py           # Stream scoring functions
│   └── types.py             # Data types and enums
├── validation/
│   ├── synthetic_validation.py   # N=1,000 validation engine
│   ├── ablation_study.py         # 324-config weight ablation
│   └── results/                  # Validation outputs
├── examples/
│   ├── cybersecurity.py
│   ├── healthcare.py
│   ├── financial.py
│   └── defense.py
├── docs/
│   └── VERDICT_WEIGHT_Paper.pdf  # SSRN 6532658
├── setup.py
├── requirements.txt
└── README.md

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Citation

If you use VERDICT WEIGHT™ in your research, please cite:

@misc{byrd2026verdictweight,
  title={VERDICT WEIGHT: A Context-Adaptive Multi-Source Confidence Synthesis 
         Framework for Autonomous AI Intelligence Systems},
  author={Byrd, Andre},
  year={2026},
  howpublished={SSRN},
  note={Abstract ID: 6532658},
  url={https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6532658}
}

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Legal

VERDICT WEIGHT™ is a trademark of Six Sense Enterprise Services LLC (Odingard Security). USPTO Serial Number: 99747827.

© 2026 Odingard Security / Six Sense Enterprise Services LLC. All rights reserved.

For licensing inquiries: [e-mail]