verdict-weight 1.0.1

VERDICT WEIGHT™ — Context-Adaptive Multi-Source Confidence Synthesis. 57.8% adversarial suppression. N=10,000 validated. 12 domain profiles.

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."

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The Problem

Autonomous AI systems treat all intelligence sources as 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.

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Validated Results

Validated across N=10,000 synthetic scenarios (seed=42, fully reproducible). Dataset SHA-256: 40bc6e227e30f5292796b3c8df60c68a8339180eea4e2379f1ab9d1e5ac8bd63

Method	Brier ↓	95% CI	AUC-ROC	McNemar
VERDICT WEIGHT™	0.2079	[0.2036, 0.2122]	0.7499	—
Equal Weight	0.2170	[0.2130, 0.2210]	0.7450	p<0.001 ***
Single Source	0.2499	[0.2447, 0.2553]	0.6537	p<0.001 ***
Two Stream	0.2298	[0.2251, 0.2346]	0.7258	p<0.001 ***

Key results:

• 57.8% suppression of adversarial spoofed intelligence vs single-source baselines
• +16.8% Brier Score improvement vs single source (p<0.001 ***)
• +14.7% AUC-ROC improvement vs single source (p<0.001 ***)
• 5-fold CV stability: Brier 0.2079 ± 0.0046 — results do not overfit
• All significance tests: McNemar p=0.000000 against all baselines

Cross-Vertical Performance (N=1,000 per vertical)

Vertical	Brier Δ%	AUC Δ%	Adv. Suppression
Cybersecurity	+17.7%	+17.0%	57.4%
Healthcare	+9.6%	+20.9%	—
Financial	+10.3%	+15.1%	—
Manufacturing	+9.2%	+8.5%	40.5%
Legal	+4.4%	+3.8%	30.7%
Defense	-2.5%	+0.3%	16.6%
Enterprise RAG	-6.7%	-1.1%	25.7%

Defense and RAG show negative Brier improvement due to synthetic data characteristics. See audit report for full failure mode analysis.

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What VERDICT WEIGHT™ Does

Four evidence streams → Three outputs → One decision.

Four Evidence Streams

Stream	Symbol	Description
Source Reliability	SR	Credibility of the originating source (0.01–0.99)
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	Range	Description
Signal Strength	SS	0–1	Confidence the signal is real
Doubt Index	DI	0–1	Inter-stream disagreement
Consequence Weight	CW	0–1	Actionability after doubt adjustment

Twelve Context Profiles

Domain	Profile Type
Cybersecurity (General)	Corroboration-dominant
Cybersecurity (APT)	Source reliability elevated, slow decay
Cybersecurity (Zero-Day)	Temporal decay dominant
Cybersecurity (Disinformation)	Maximum corroboration + 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 strategic 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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Quick Start

pip install verdict-weight

from verdict_weight import VerdictWeight, ContextType

vw = VerdictWeight()

# Score a cybersecurity threat intelligence signal
result = vw.score(
    source_reliability=0.92,        # How credible is this source?
    n_corroborating_sources=3,       # How many independent sources confirm?
    age_value=2.5,                   # How old is this intelligence (days)?
    correct_predictions=45,          # Source's historical correct calls
    total_predictions=50,            # Source's total historical calls
    context=ContextType.CYBERSECURITY_APT
)

print(result.action_tier)           # CRITICAL
print(result.consequence_weight)    # 0.8380
print(result.doubt_index)           # 0.0691
print(result.interpretation)        # "Act immediately. High-confidence..."
print(result.to_json())             # Full JSON output

Adversarial signal — watch the suppression

# High-credibility source but zero corroboration — spoofed intel
result = vw.score(
    source_reliability=0.95,         # Looks credible
    n_corroborating_sources=0,        # Nobody else is confirming this
    age_value=1.0,
    context=ContextType.CYBERSECURITY_DISINFO
)

print(result.action_tier)           # NOISE
print(result.consequence_weight)    # 0.147 — suppressed by 84%

Score pre-computed streams directly

result = vw.score_streams(
    SR=0.92, CC=0.78, TD=0.94, HA=0.88,
    context=ContextType.FINANCIAL_FRAUD
)

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Repository Structure

verdict-weight/
├── verdict_weight/
│   ├── __init__.py          # Public API
│   └── core.py              # VerdictWeight engine — all 12 profiles
├── validation/
│   ├── synthetic_validation.py   # N=10,000 validation (seed=42)
│   └── ablation_study.py         # 324-config weight ablation
├── examples/
│   ├── cybersecurity.py
│   └── healthcare.py
├── docs/
│   └── VERDICT_WEIGHT_Paper.pdf  # SSRN 6532658
├── pyproject.toml
├── requirements.txt
└── README.md

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Mathematical Foundation

Signal Strength (weighted geometric mean):

SS = ∏(S_i + ε)^w_i   where Σw_i = 1.0

Doubt Index (normalized coefficient of variation):

DI = clip(σ(SR,CC,TD,HA) / μ(SR,CC,TD,HA), 0, 1)

Consequence Weight:

CW = clip(SS × (1 - δ × DI), 0, 1)

The geometric mean is chosen because it penalizes weak streams multiplicatively. A single stream near zero collapses the score — preventing one strong source from masking fundamental evidence gaps. This is the structural guarantee behind the 57.8% adversarial suppression result.

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Citation

@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 Preprint},
  note={SSRN Abstract ID: 6532658},
  url={https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6532658},
  doi={10.5281/zenodo.19447547}
}

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Reproducibility

Results are fully reproducible:

1. Clone this repository
2. Run python validation/synthetic_validation.py
3. Verify SHA-256 matches: 40bc6e227e30f5292796b3c8df60c68a8339180eea4e2379f1ab9d1e5ac8bd63

Master seed: 42 (never changes — all results are deterministic)

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Legal

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

© 2026 Six Sense Enterprise Services LLC. All rights reserved.

This software is made available for research and evaluation purposes. Commercial deployment requires a license agreement.

For licensing: [e-mail] Paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6532658 DOI: https://doi.org/10.5281/zenodo.19447547